diff options
Diffstat (limited to 'plugins/AdvaImg/src/LibJPEG/jquant2.c')
| -rw-r--r-- | plugins/AdvaImg/src/LibJPEG/jquant2.c | 2622 |
1 files changed, 1311 insertions, 1311 deletions
diff --git a/plugins/AdvaImg/src/LibJPEG/jquant2.c b/plugins/AdvaImg/src/LibJPEG/jquant2.c index 38fc2af7a5..f7e351f2ac 100644 --- a/plugins/AdvaImg/src/LibJPEG/jquant2.c +++ b/plugins/AdvaImg/src/LibJPEG/jquant2.c @@ -1,1311 +1,1311 @@ -/* - * jquant2.c - * - * Copyright (C) 1991-1996, Thomas G. Lane. - * Modified 2011 by Guido Vollbeding. - * This file is part of the Independent JPEG Group's software. - * For conditions of distribution and use, see the accompanying README file. - * - * This file contains 2-pass color quantization (color mapping) routines. - * These routines provide selection of a custom color map for an image, - * followed by mapping of the image to that color map, with optional - * Floyd-Steinberg dithering. - * It is also possible to use just the second pass to map to an arbitrary - * externally-given color map. - * - * Note: ordered dithering is not supported, since there isn't any fast - * way to compute intercolor distances; it's unclear that ordered dither's - * fundamental assumptions even hold with an irregularly spaced color map. - */ - -#define JPEG_INTERNALS -#include "jinclude.h" -#include "jpeglib.h" - -#ifdef QUANT_2PASS_SUPPORTED - - -/* - * This module implements the well-known Heckbert paradigm for color - * quantization. Most of the ideas used here can be traced back to - * Heckbert's seminal paper - * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display", - * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304. - * - * In the first pass over the image, we accumulate a histogram showing the - * usage count of each possible color. To keep the histogram to a reasonable - * size, we reduce the precision of the input; typical practice is to retain - * 5 or 6 bits per color, so that 8 or 4 different input values are counted - * in the same histogram cell. - * - * Next, the color-selection step begins with a box representing the whole - * color space, and repeatedly splits the "largest" remaining box until we - * have as many boxes as desired colors. Then the mean color in each - * remaining box becomes one of the possible output colors. - * - * The second pass over the image maps each input pixel to the closest output - * color (optionally after applying a Floyd-Steinberg dithering correction). - * This mapping is logically trivial, but making it go fast enough requires - * considerable care. - * - * Heckbert-style quantizers vary a good deal in their policies for choosing - * the "largest" box and deciding where to cut it. The particular policies - * used here have proved out well in experimental comparisons, but better ones - * may yet be found. - * - * In earlier versions of the IJG code, this module quantized in YCbCr color - * space, processing the raw upsampled data without a color conversion step. - * This allowed the color conversion math to be done only once per colormap - * entry, not once per pixel. However, that optimization precluded other - * useful optimizations (such as merging color conversion with upsampling) - * and it also interfered with desired capabilities such as quantizing to an - * externally-supplied colormap. We have therefore abandoned that approach. - * The present code works in the post-conversion color space, typically RGB. - * - * To improve the visual quality of the results, we actually work in scaled - * RGB space, giving G distances more weight than R, and R in turn more than - * B. To do everything in integer math, we must use integer scale factors. - * The 2/3/1 scale factors used here correspond loosely to the relative - * weights of the colors in the NTSC grayscale equation. - * If you want to use this code to quantize a non-RGB color space, you'll - * probably need to change these scale factors. - */ - -#define R_SCALE 2 /* scale R distances by this much */ -#define G_SCALE 3 /* scale G distances by this much */ -#define B_SCALE 1 /* and B by this much */ - -/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined - * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B - * and B,G,R orders. If you define some other weird order in jmorecfg.h, - * you'll get compile errors until you extend this logic. In that case - * you'll probably want to tweak the histogram sizes too. - */ - -#if RGB_RED == 0 -#define C0_SCALE R_SCALE -#endif -#if RGB_BLUE == 0 -#define C0_SCALE B_SCALE -#endif -#if RGB_GREEN == 1 -#define C1_SCALE G_SCALE -#endif -#if RGB_RED == 2 -#define C2_SCALE R_SCALE -#endif -#if RGB_BLUE == 2 -#define C2_SCALE B_SCALE -#endif - - -/* - * First we have the histogram data structure and routines for creating it. - * - * The number of bits of precision can be adjusted by changing these symbols. - * We recommend keeping 6 bits for G and 5 each for R and B. - * If you have plenty of memory and cycles, 6 bits all around gives marginally - * better results; if you are short of memory, 5 bits all around will save - * some space but degrade the results. - * To maintain a fully accurate histogram, we'd need to allocate a "long" - * (preferably unsigned long) for each cell. In practice this is overkill; - * we can get by with 16 bits per cell. Few of the cell counts will overflow, - * and clamping those that do overflow to the maximum value will give close- - * enough results. This reduces the recommended histogram size from 256Kb - * to 128Kb, which is a useful savings on PC-class machines. - * (In the second pass the histogram space is re-used for pixel mapping data; - * in that capacity, each cell must be able to store zero to the number of - * desired colors. 16 bits/cell is plenty for that too.) - * Since the JPEG code is intended to run in small memory model on 80x86 - * machines, we can't just allocate the histogram in one chunk. Instead - * of a true 3-D array, we use a row of pointers to 2-D arrays. Each - * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and - * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that - * on 80x86 machines, the pointer row is in near memory but the actual - * arrays are in far memory (same arrangement as we use for image arrays). - */ - -#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */ - -/* These will do the right thing for either R,G,B or B,G,R color order, - * but you may not like the results for other color orders. - */ -#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */ -#define HIST_C1_BITS 6 /* bits of precision in G histogram */ -#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */ - -/* Number of elements along histogram axes. */ -#define HIST_C0_ELEMS (1<<HIST_C0_BITS) -#define HIST_C1_ELEMS (1<<HIST_C1_BITS) -#define HIST_C2_ELEMS (1<<HIST_C2_BITS) - -/* These are the amounts to shift an input value to get a histogram index. */ -#define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS) -#define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS) -#define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS) - - -typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */ - -typedef histcell FAR * histptr; /* for pointers to histogram cells */ - -typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */ -typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */ -typedef hist2d * hist3d; /* type for top-level pointer */ - - -/* Declarations for Floyd-Steinberg dithering. - * - * Errors are accumulated into the array fserrors[], at a resolution of - * 1/16th of a pixel count. The error at a given pixel is propagated - * to its not-yet-processed neighbors using the standard F-S fractions, - * ... (here) 7/16 - * 3/16 5/16 1/16 - * We work left-to-right on even rows, right-to-left on odd rows. - * - * We can get away with a single array (holding one row's worth of errors) - * by using it to store the current row's errors at pixel columns not yet - * processed, but the next row's errors at columns already processed. We - * need only a few extra variables to hold the errors immediately around the - * current column. (If we are lucky, those variables are in registers, but - * even if not, they're probably cheaper to access than array elements are.) - * - * The fserrors[] array has (#columns + 2) entries; the extra entry at - * each end saves us from special-casing the first and last pixels. - * Each entry is three values long, one value for each color component. - * - * Note: on a wide image, we might not have enough room in a PC's near data - * segment to hold the error array; so it is allocated with alloc_large. - */ - -#if BITS_IN_JSAMPLE == 8 -typedef INT16 FSERROR; /* 16 bits should be enough */ -typedef int LOCFSERROR; /* use 'int' for calculation temps */ -#else -typedef INT32 FSERROR; /* may need more than 16 bits */ -typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */ -#endif - -typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */ - - -/* Private subobject */ - -typedef struct { - struct jpeg_color_quantizer pub; /* public fields */ - - /* Space for the eventually created colormap is stashed here */ - JSAMPARRAY sv_colormap; /* colormap allocated at init time */ - int desired; /* desired # of colors = size of colormap */ - - /* Variables for accumulating image statistics */ - hist3d histogram; /* pointer to the histogram */ - - boolean needs_zeroed; /* TRUE if next pass must zero histogram */ - - /* Variables for Floyd-Steinberg dithering */ - FSERRPTR fserrors; /* accumulated errors */ - boolean on_odd_row; /* flag to remember which row we are on */ - int * error_limiter; /* table for clamping the applied error */ -} my_cquantizer; - -typedef my_cquantizer * my_cquantize_ptr; - - -/* - * Prescan some rows of pixels. - * In this module the prescan simply updates the histogram, which has been - * initialized to zeroes by start_pass. - * An output_buf parameter is required by the method signature, but no data - * is actually output (in fact the buffer controller is probably passing a - * NULL pointer). - */ - -METHODDEF(void) -prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf, - JSAMPARRAY output_buf, int num_rows) -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - register JSAMPROW ptr; - register histptr histp; - register hist3d histogram = cquantize->histogram; - int row; - JDIMENSION col; - JDIMENSION width = cinfo->output_width; - - for (row = 0; row < num_rows; row++) { - ptr = input_buf[row]; - for (col = width; col > 0; col--) { - /* get pixel value and index into the histogram */ - histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT] - [GETJSAMPLE(ptr[1]) >> C1_SHIFT] - [GETJSAMPLE(ptr[2]) >> C2_SHIFT]; - /* increment, check for overflow and undo increment if so. */ - if (++(*histp) <= 0) - (*histp)--; - ptr += 3; - } - } -} - - -/* - * Next we have the really interesting routines: selection of a colormap - * given the completed histogram. - * These routines work with a list of "boxes", each representing a rectangular - * subset of the input color space (to histogram precision). - */ - -typedef struct { - /* The bounds of the box (inclusive); expressed as histogram indexes */ - int c0min, c0max; - int c1min, c1max; - int c2min, c2max; - /* The volume (actually 2-norm) of the box */ - INT32 volume; - /* The number of nonzero histogram cells within this box */ - long colorcount; -} box; - -typedef box * boxptr; - - -LOCAL(boxptr) -find_biggest_color_pop (boxptr boxlist, int numboxes) -/* Find the splittable box with the largest color population */ -/* Returns NULL if no splittable boxes remain */ -{ - register boxptr boxp; - register int i; - register long maxc = 0; - boxptr which = NULL; - - for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { - if (boxp->colorcount > maxc && boxp->volume > 0) { - which = boxp; - maxc = boxp->colorcount; - } - } - return which; -} - - -LOCAL(boxptr) -find_biggest_volume (boxptr boxlist, int numboxes) -/* Find the splittable box with the largest (scaled) volume */ -/* Returns NULL if no splittable boxes remain */ -{ - register boxptr boxp; - register int i; - register INT32 maxv = 0; - boxptr which = NULL; - - for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { - if (boxp->volume > maxv) { - which = boxp; - maxv = boxp->volume; - } - } - return which; -} - - -LOCAL(void) -update_box (j_decompress_ptr cinfo, boxptr boxp) -/* Shrink the min/max bounds of a box to enclose only nonzero elements, */ -/* and recompute its volume and population */ -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - hist3d histogram = cquantize->histogram; - histptr histp; - int c0,c1,c2; - int c0min,c0max,c1min,c1max,c2min,c2max; - INT32 dist0,dist1,dist2; - long ccount; - - c0min = boxp->c0min; c0max = boxp->c0max; - c1min = boxp->c1min; c1max = boxp->c1max; - c2min = boxp->c2min; c2max = boxp->c2max; - - if (c0max > c0min) - for (c0 = c0min; c0 <= c0max; c0++) - for (c1 = c1min; c1 <= c1max; c1++) { - histp = & histogram[c0][c1][c2min]; - for (c2 = c2min; c2 <= c2max; c2++) - if (*histp++ != 0) { - boxp->c0min = c0min = c0; - goto have_c0min; - } - } - have_c0min: - if (c0max > c0min) - for (c0 = c0max; c0 >= c0min; c0--) - for (c1 = c1min; c1 <= c1max; c1++) { - histp = & histogram[c0][c1][c2min]; - for (c2 = c2min; c2 <= c2max; c2++) - if (*histp++ != 0) { - boxp->c0max = c0max = c0; - goto have_c0max; - } - } - have_c0max: - if (c1max > c1min) - for (c1 = c1min; c1 <= c1max; c1++) - for (c0 = c0min; c0 <= c0max; c0++) { - histp = & histogram[c0][c1][c2min]; - for (c2 = c2min; c2 <= c2max; c2++) - if (*histp++ != 0) { - boxp->c1min = c1min = c1; - goto have_c1min; - } - } - have_c1min: - if (c1max > c1min) - for (c1 = c1max; c1 >= c1min; c1--) - for (c0 = c0min; c0 <= c0max; c0++) { - histp = & histogram[c0][c1][c2min]; - for (c2 = c2min; c2 <= c2max; c2++) - if (*histp++ != 0) { - boxp->c1max = c1max = c1; - goto have_c1max; - } - } - have_c1max: - if (c2max > c2min) - for (c2 = c2min; c2 <= c2max; c2++) - for (c0 = c0min; c0 <= c0max; c0++) { - histp = & histogram[c0][c1min][c2]; - for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) - if (*histp != 0) { - boxp->c2min = c2min = c2; - goto have_c2min; - } - } - have_c2min: - if (c2max > c2min) - for (c2 = c2max; c2 >= c2min; c2--) - for (c0 = c0min; c0 <= c0max; c0++) { - histp = & histogram[c0][c1min][c2]; - for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) - if (*histp != 0) { - boxp->c2max = c2max = c2; - goto have_c2max; - } - } - have_c2max: - - /* Update box volume. - * We use 2-norm rather than real volume here; this biases the method - * against making long narrow boxes, and it has the side benefit that - * a box is splittable iff norm > 0. - * Since the differences are expressed in histogram-cell units, - * we have to shift back to JSAMPLE units to get consistent distances; - * after which, we scale according to the selected distance scale factors. - */ - dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; - dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; - dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; - boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2; - - /* Now scan remaining volume of box and compute population */ - ccount = 0; - for (c0 = c0min; c0 <= c0max; c0++) - for (c1 = c1min; c1 <= c1max; c1++) { - histp = & histogram[c0][c1][c2min]; - for (c2 = c2min; c2 <= c2max; c2++, histp++) - if (*histp != 0) { - ccount++; - } - } - boxp->colorcount = ccount; -} - - -LOCAL(int) -median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes, - int desired_colors) -/* Repeatedly select and split the largest box until we have enough boxes */ -{ - int n,lb; - int c0,c1,c2,cmax; - register boxptr b1,b2; - - while (numboxes < desired_colors) { - /* Select box to split. - * Current algorithm: by population for first half, then by volume. - */ - if (numboxes*2 <= desired_colors) { - b1 = find_biggest_color_pop(boxlist, numboxes); - } else { - b1 = find_biggest_volume(boxlist, numboxes); - } - if (b1 == NULL) /* no splittable boxes left! */ - break; - b2 = &boxlist[numboxes]; /* where new box will go */ - /* Copy the color bounds to the new box. */ - b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max; - b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min; - /* Choose which axis to split the box on. - * Current algorithm: longest scaled axis. - * See notes in update_box about scaling distances. - */ - c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE; - c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE; - c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE; - /* We want to break any ties in favor of green, then red, blue last. - * This code does the right thing for R,G,B or B,G,R color orders only. - */ -#if RGB_RED == 0 - cmax = c1; n = 1; - if (c0 > cmax) { cmax = c0; n = 0; } - if (c2 > cmax) { n = 2; } -#else - cmax = c1; n = 1; - if (c2 > cmax) { cmax = c2; n = 2; } - if (c0 > cmax) { n = 0; } -#endif - /* Choose split point along selected axis, and update box bounds. - * Current algorithm: split at halfway point. - * (Since the box has been shrunk to minimum volume, - * any split will produce two nonempty subboxes.) - * Note that lb value is max for lower box, so must be < old max. - */ - switch (n) { - case 0: - lb = (b1->c0max + b1->c0min) / 2; - b1->c0max = lb; - b2->c0min = lb+1; - break; - case 1: - lb = (b1->c1max + b1->c1min) / 2; - b1->c1max = lb; - b2->c1min = lb+1; - break; - case 2: - lb = (b1->c2max + b1->c2min) / 2; - b1->c2max = lb; - b2->c2min = lb+1; - break; - } - /* Update stats for boxes */ - update_box(cinfo, b1); - update_box(cinfo, b2); - numboxes++; - } - return numboxes; -} - - -LOCAL(void) -compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor) -/* Compute representative color for a box, put it in colormap[icolor] */ -{ - /* Current algorithm: mean weighted by pixels (not colors) */ - /* Note it is important to get the rounding correct! */ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - hist3d histogram = cquantize->histogram; - histptr histp; - int c0,c1,c2; - int c0min,c0max,c1min,c1max,c2min,c2max; - long count; - long total = 0; - long c0total = 0; - long c1total = 0; - long c2total = 0; - - c0min = boxp->c0min; c0max = boxp->c0max; - c1min = boxp->c1min; c1max = boxp->c1max; - c2min = boxp->c2min; c2max = boxp->c2max; - - for (c0 = c0min; c0 <= c0max; c0++) - for (c1 = c1min; c1 <= c1max; c1++) { - histp = & histogram[c0][c1][c2min]; - for (c2 = c2min; c2 <= c2max; c2++) { - if ((count = *histp++) != 0) { - total += count; - c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count; - c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count; - c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count; - } - } - } - - cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); - cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); - cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); -} - - -LOCAL(void) -select_colors (j_decompress_ptr cinfo, int desired_colors) -/* Master routine for color selection */ -{ - boxptr boxlist; - int numboxes; - int i; - - /* Allocate workspace for box list */ - boxlist = (boxptr) (*cinfo->mem->alloc_small) - ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box)); - /* Initialize one box containing whole space */ - numboxes = 1; - boxlist[0].c0min = 0; - boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; - boxlist[0].c1min = 0; - boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; - boxlist[0].c2min = 0; - boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; - /* Shrink it to actually-used volume and set its statistics */ - update_box(cinfo, & boxlist[0]); - /* Perform median-cut to produce final box list */ - numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors); - /* Compute the representative color for each box, fill colormap */ - for (i = 0; i < numboxes; i++) - compute_color(cinfo, & boxlist[i], i); - cinfo->actual_number_of_colors = numboxes; - TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes); -} - - -/* - * These routines are concerned with the time-critical task of mapping input - * colors to the nearest color in the selected colormap. - * - * We re-use the histogram space as an "inverse color map", essentially a - * cache for the results of nearest-color searches. All colors within a - * histogram cell will be mapped to the same colormap entry, namely the one - * closest to the cell's center. This may not be quite the closest entry to - * the actual input color, but it's almost as good. A zero in the cache - * indicates we haven't found the nearest color for that cell yet; the array - * is cleared to zeroes before starting the mapping pass. When we find the - * nearest color for a cell, its colormap index plus one is recorded in the - * cache for future use. The pass2 scanning routines call fill_inverse_cmap - * when they need to use an unfilled entry in the cache. - * - * Our method of efficiently finding nearest colors is based on the "locally - * sorted search" idea described by Heckbert and on the incremental distance - * calculation described by Spencer W. Thomas in chapter III.1 of Graphics - * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that - * the distances from a given colormap entry to each cell of the histogram can - * be computed quickly using an incremental method: the differences between - * distances to adjacent cells themselves differ by a constant. This allows a - * fairly fast implementation of the "brute force" approach of computing the - * distance from every colormap entry to every histogram cell. Unfortunately, - * it needs a work array to hold the best-distance-so-far for each histogram - * cell (because the inner loop has to be over cells, not colormap entries). - * The work array elements have to be INT32s, so the work array would need - * 256Kb at our recommended precision. This is not feasible in DOS machines. - * - * To get around these problems, we apply Thomas' method to compute the - * nearest colors for only the cells within a small subbox of the histogram. - * The work array need be only as big as the subbox, so the memory usage - * problem is solved. Furthermore, we need not fill subboxes that are never - * referenced in pass2; many images use only part of the color gamut, so a - * fair amount of work is saved. An additional advantage of this - * approach is that we can apply Heckbert's locality criterion to quickly - * eliminate colormap entries that are far away from the subbox; typically - * three-fourths of the colormap entries are rejected by Heckbert's criterion, - * and we need not compute their distances to individual cells in the subbox. - * The speed of this approach is heavily influenced by the subbox size: too - * small means too much overhead, too big loses because Heckbert's criterion - * can't eliminate as many colormap entries. Empirically the best subbox - * size seems to be about 1/512th of the histogram (1/8th in each direction). - * - * Thomas' article also describes a refined method which is asymptotically - * faster than the brute-force method, but it is also far more complex and - * cannot efficiently be applied to small subboxes. It is therefore not - * useful for programs intended to be portable to DOS machines. On machines - * with plenty of memory, filling the whole histogram in one shot with Thomas' - * refined method might be faster than the present code --- but then again, - * it might not be any faster, and it's certainly more complicated. - */ - - -/* log2(histogram cells in update box) for each axis; this can be adjusted */ -#define BOX_C0_LOG (HIST_C0_BITS-3) -#define BOX_C1_LOG (HIST_C1_BITS-3) -#define BOX_C2_LOG (HIST_C2_BITS-3) - -#define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */ -#define BOX_C1_ELEMS (1<<BOX_C1_LOG) -#define BOX_C2_ELEMS (1<<BOX_C2_LOG) - -#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG) -#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG) -#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG) - - -/* - * The next three routines implement inverse colormap filling. They could - * all be folded into one big routine, but splitting them up this way saves - * some stack space (the mindist[] and bestdist[] arrays need not coexist) - * and may allow some compilers to produce better code by registerizing more - * inner-loop variables. - */ - -LOCAL(int) -find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, - JSAMPLE colorlist[]) -/* Locate the colormap entries close enough to an update box to be candidates - * for the nearest entry to some cell(s) in the update box. The update box - * is specified by the center coordinates of its first cell. The number of - * candidate colormap entries is returned, and their colormap indexes are - * placed in colorlist[]. - * This routine uses Heckbert's "locally sorted search" criterion to select - * the colors that need further consideration. - */ -{ - int numcolors = cinfo->actual_number_of_colors; - int maxc0, maxc1, maxc2; - int centerc0, centerc1, centerc2; - int i, x, ncolors; - INT32 minmaxdist, min_dist, max_dist, tdist; - INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */ - - /* Compute true coordinates of update box's upper corner and center. - * Actually we compute the coordinates of the center of the upper-corner - * histogram cell, which are the upper bounds of the volume we care about. - * Note that since ">>" rounds down, the "center" values may be closer to - * min than to max; hence comparisons to them must be "<=", not "<". - */ - maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT)); - centerc0 = (minc0 + maxc0) >> 1; - maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT)); - centerc1 = (minc1 + maxc1) >> 1; - maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT)); - centerc2 = (minc2 + maxc2) >> 1; - - /* For each color in colormap, find: - * 1. its minimum squared-distance to any point in the update box - * (zero if color is within update box); - * 2. its maximum squared-distance to any point in the update box. - * Both of these can be found by considering only the corners of the box. - * We save the minimum distance for each color in mindist[]; - * only the smallest maximum distance is of interest. - */ - minmaxdist = 0x7FFFFFFFL; - - for (i = 0; i < numcolors; i++) { - /* We compute the squared-c0-distance term, then add in the other two. */ - x = GETJSAMPLE(cinfo->colormap[0][i]); - if (x < minc0) { - tdist = (x - minc0) * C0_SCALE; - min_dist = tdist*tdist; - tdist = (x - maxc0) * C0_SCALE; - max_dist = tdist*tdist; - } else if (x > maxc0) { - tdist = (x - maxc0) * C0_SCALE; - min_dist = tdist*tdist; - tdist = (x - minc0) * C0_SCALE; - max_dist = tdist*tdist; - } else { - /* within cell range so no contribution to min_dist */ - min_dist = 0; - if (x <= centerc0) { - tdist = (x - maxc0) * C0_SCALE; - max_dist = tdist*tdist; - } else { - tdist = (x - minc0) * C0_SCALE; - max_dist = tdist*tdist; - } - } - - x = GETJSAMPLE(cinfo->colormap[1][i]); - if (x < minc1) { - tdist = (x - minc1) * C1_SCALE; - min_dist += tdist*tdist; - tdist = (x - maxc1) * C1_SCALE; - max_dist += tdist*tdist; - } else if (x > maxc1) { - tdist = (x - maxc1) * C1_SCALE; - min_dist += tdist*tdist; - tdist = (x - minc1) * C1_SCALE; - max_dist += tdist*tdist; - } else { - /* within cell range so no contribution to min_dist */ - if (x <= centerc1) { - tdist = (x - maxc1) * C1_SCALE; - max_dist += tdist*tdist; - } else { - tdist = (x - minc1) * C1_SCALE; - max_dist += tdist*tdist; - } - } - - x = GETJSAMPLE(cinfo->colormap[2][i]); - if (x < minc2) { - tdist = (x - minc2) * C2_SCALE; - min_dist += tdist*tdist; - tdist = (x - maxc2) * C2_SCALE; - max_dist += tdist*tdist; - } else if (x > maxc2) { - tdist = (x - maxc2) * C2_SCALE; - min_dist += tdist*tdist; - tdist = (x - minc2) * C2_SCALE; - max_dist += tdist*tdist; - } else { - /* within cell range so no contribution to min_dist */ - if (x <= centerc2) { - tdist = (x - maxc2) * C2_SCALE; - max_dist += tdist*tdist; - } else { - tdist = (x - minc2) * C2_SCALE; - max_dist += tdist*tdist; - } - } - - mindist[i] = min_dist; /* save away the results */ - if (max_dist < minmaxdist) - minmaxdist = max_dist; - } - - /* Now we know that no cell in the update box is more than minmaxdist - * away from some colormap entry. Therefore, only colors that are - * within minmaxdist of some part of the box need be considered. - */ - ncolors = 0; - for (i = 0; i < numcolors; i++) { - if (mindist[i] <= minmaxdist) - colorlist[ncolors++] = (JSAMPLE) i; - } - return ncolors; -} - - -LOCAL(void) -find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, - int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[]) -/* Find the closest colormap entry for each cell in the update box, - * given the list of candidate colors prepared by find_nearby_colors. - * Return the indexes of the closest entries in the bestcolor[] array. - * This routine uses Thomas' incremental distance calculation method to - * find the distance from a colormap entry to successive cells in the box. - */ -{ - int ic0, ic1, ic2; - int i, icolor; - register INT32 * bptr; /* pointer into bestdist[] array */ - JSAMPLE * cptr; /* pointer into bestcolor[] array */ - INT32 dist0, dist1; /* initial distance values */ - register INT32 dist2; /* current distance in inner loop */ - INT32 xx0, xx1; /* distance increments */ - register INT32 xx2; - INT32 inc0, inc1, inc2; /* initial values for increments */ - /* This array holds the distance to the nearest-so-far color for each cell */ - INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; - - /* Initialize best-distance for each cell of the update box */ - bptr = bestdist; - for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--) - *bptr++ = 0x7FFFFFFFL; - - /* For each color selected by find_nearby_colors, - * compute its distance to the center of each cell in the box. - * If that's less than best-so-far, update best distance and color number. - */ - - /* Nominal steps between cell centers ("x" in Thomas article) */ -#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) -#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) -#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) - - for (i = 0; i < numcolors; i++) { - icolor = GETJSAMPLE(colorlist[i]); - /* Compute (square of) distance from minc0/c1/c2 to this color */ - inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE; - dist0 = inc0*inc0; - inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE; - dist0 += inc1*inc1; - inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE; - dist0 += inc2*inc2; - /* Form the initial difference increments */ - inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; - inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; - inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; - /* Now loop over all cells in box, updating distance per Thomas method */ - bptr = bestdist; - cptr = bestcolor; - xx0 = inc0; - for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) { - dist1 = dist0; - xx1 = inc1; - for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) { - dist2 = dist1; - xx2 = inc2; - for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) { - if (dist2 < *bptr) { - *bptr = dist2; - *cptr = (JSAMPLE) icolor; - } - dist2 += xx2; - xx2 += 2 * STEP_C2 * STEP_C2; - bptr++; - cptr++; - } - dist1 += xx1; - xx1 += 2 * STEP_C1 * STEP_C1; - } - dist0 += xx0; - xx0 += 2 * STEP_C0 * STEP_C0; - } - } -} - - -LOCAL(void) -fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2) -/* Fill the inverse-colormap entries in the update box that contains */ -/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */ -/* we can fill as many others as we wish.) */ -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - hist3d histogram = cquantize->histogram; - int minc0, minc1, minc2; /* lower left corner of update box */ - int ic0, ic1, ic2; - register JSAMPLE * cptr; /* pointer into bestcolor[] array */ - register histptr cachep; /* pointer into main cache array */ - /* This array lists the candidate colormap indexes. */ - JSAMPLE colorlist[MAXNUMCOLORS]; - int numcolors; /* number of candidate colors */ - /* This array holds the actually closest colormap index for each cell. */ - JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; - - /* Convert cell coordinates to update box ID */ - c0 >>= BOX_C0_LOG; - c1 >>= BOX_C1_LOG; - c2 >>= BOX_C2_LOG; - - /* Compute true coordinates of update box's origin corner. - * Actually we compute the coordinates of the center of the corner - * histogram cell, which are the lower bounds of the volume we care about. - */ - minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); - minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); - minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); - - /* Determine which colormap entries are close enough to be candidates - * for the nearest entry to some cell in the update box. - */ - numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist); - - /* Determine the actually nearest colors. */ - find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist, - bestcolor); - - /* Save the best color numbers (plus 1) in the main cache array */ - c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */ - c1 <<= BOX_C1_LOG; - c2 <<= BOX_C2_LOG; - cptr = bestcolor; - for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) { - for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) { - cachep = & histogram[c0+ic0][c1+ic1][c2]; - for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) { - *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); - } - } - } -} - - -/* - * Map some rows of pixels to the output colormapped representation. - */ - -METHODDEF(void) -pass2_no_dither (j_decompress_ptr cinfo, - JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) -/* This version performs no dithering */ -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - hist3d histogram = cquantize->histogram; - register JSAMPROW inptr, outptr; - register histptr cachep; - register int c0, c1, c2; - int row; - JDIMENSION col; - JDIMENSION width = cinfo->output_width; - - for (row = 0; row < num_rows; row++) { - inptr = input_buf[row]; - outptr = output_buf[row]; - for (col = width; col > 0; col--) { - /* get pixel value and index into the cache */ - c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT; - c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT; - c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT; - cachep = & histogram[c0][c1][c2]; - /* If we have not seen this color before, find nearest colormap entry */ - /* and update the cache */ - if (*cachep == 0) - fill_inverse_cmap(cinfo, c0,c1,c2); - /* Now emit the colormap index for this cell */ - *outptr++ = (JSAMPLE) (*cachep - 1); - } - } -} - - -METHODDEF(void) -pass2_fs_dither (j_decompress_ptr cinfo, - JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) -/* This version performs Floyd-Steinberg dithering */ -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - hist3d histogram = cquantize->histogram; - register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */ - LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */ - LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */ - register FSERRPTR errorptr; /* => fserrors[] at column before current */ - JSAMPROW inptr; /* => current input pixel */ - JSAMPROW outptr; /* => current output pixel */ - histptr cachep; - int dir; /* +1 or -1 depending on direction */ - int dir3; /* 3*dir, for advancing inptr & errorptr */ - int row; - JDIMENSION col; - JDIMENSION width = cinfo->output_width; - JSAMPLE *range_limit = cinfo->sample_range_limit; - int *error_limit = cquantize->error_limiter; - JSAMPROW colormap0 = cinfo->colormap[0]; - JSAMPROW colormap1 = cinfo->colormap[1]; - JSAMPROW colormap2 = cinfo->colormap[2]; - SHIFT_TEMPS - - for (row = 0; row < num_rows; row++) { - inptr = input_buf[row]; - outptr = output_buf[row]; - if (cquantize->on_odd_row) { - /* work right to left in this row */ - inptr += (width-1) * 3; /* so point to rightmost pixel */ - outptr += width-1; - dir = -1; - dir3 = -3; - errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */ - cquantize->on_odd_row = FALSE; /* flip for next time */ - } else { - /* work left to right in this row */ - dir = 1; - dir3 = 3; - errorptr = cquantize->fserrors; /* => entry before first real column */ - cquantize->on_odd_row = TRUE; /* flip for next time */ - } - /* Preset error values: no error propagated to first pixel from left */ - cur0 = cur1 = cur2 = 0; - /* and no error propagated to row below yet */ - belowerr0 = belowerr1 = belowerr2 = 0; - bpreverr0 = bpreverr1 = bpreverr2 = 0; - - for (col = width; col > 0; col--) { - /* curN holds the error propagated from the previous pixel on the - * current line. Add the error propagated from the previous line - * to form the complete error correction term for this pixel, and - * round the error term (which is expressed * 16) to an integer. - * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct - * for either sign of the error value. - * Note: errorptr points to *previous* column's array entry. - */ - cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4); - cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4); - cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4); - /* Limit the error using transfer function set by init_error_limit. - * See comments with init_error_limit for rationale. - */ - cur0 = error_limit[cur0]; - cur1 = error_limit[cur1]; - cur2 = error_limit[cur2]; - /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE. - * The maximum error is +- MAXJSAMPLE (or less with error limiting); - * this sets the required size of the range_limit array. - */ - cur0 += GETJSAMPLE(inptr[0]); - cur1 += GETJSAMPLE(inptr[1]); - cur2 += GETJSAMPLE(inptr[2]); - cur0 = GETJSAMPLE(range_limit[cur0]); - cur1 = GETJSAMPLE(range_limit[cur1]); - cur2 = GETJSAMPLE(range_limit[cur2]); - /* Index into the cache with adjusted pixel value */ - cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT]; - /* If we have not seen this color before, find nearest colormap */ - /* entry and update the cache */ - if (*cachep == 0) - fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT); - /* Now emit the colormap index for this cell */ - { register int pixcode = *cachep - 1; - *outptr = (JSAMPLE) pixcode; - /* Compute representation error for this pixel */ - cur0 -= GETJSAMPLE(colormap0[pixcode]); - cur1 -= GETJSAMPLE(colormap1[pixcode]); - cur2 -= GETJSAMPLE(colormap2[pixcode]); - } - /* Compute error fractions to be propagated to adjacent pixels. - * Add these into the running sums, and simultaneously shift the - * next-line error sums left by 1 column. - */ - { register LOCFSERROR bnexterr, delta; - - bnexterr = cur0; /* Process component 0 */ - delta = cur0 * 2; - cur0 += delta; /* form error * 3 */ - errorptr[0] = (FSERROR) (bpreverr0 + cur0); - cur0 += delta; /* form error * 5 */ - bpreverr0 = belowerr0 + cur0; - belowerr0 = bnexterr; - cur0 += delta; /* form error * 7 */ - bnexterr = cur1; /* Process component 1 */ - delta = cur1 * 2; - cur1 += delta; /* form error * 3 */ - errorptr[1] = (FSERROR) (bpreverr1 + cur1); - cur1 += delta; /* form error * 5 */ - bpreverr1 = belowerr1 + cur1; - belowerr1 = bnexterr; - cur1 += delta; /* form error * 7 */ - bnexterr = cur2; /* Process component 2 */ - delta = cur2 * 2; - cur2 += delta; /* form error * 3 */ - errorptr[2] = (FSERROR) (bpreverr2 + cur2); - cur2 += delta; /* form error * 5 */ - bpreverr2 = belowerr2 + cur2; - belowerr2 = bnexterr; - cur2 += delta; /* form error * 7 */ - } - /* At this point curN contains the 7/16 error value to be propagated - * to the next pixel on the current line, and all the errors for the - * next line have been shifted over. We are therefore ready to move on. - */ - inptr += dir3; /* Advance pixel pointers to next column */ - outptr += dir; - errorptr += dir3; /* advance errorptr to current column */ - } - /* Post-loop cleanup: we must unload the final error values into the - * final fserrors[] entry. Note we need not unload belowerrN because - * it is for the dummy column before or after the actual array. - */ - errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */ - errorptr[1] = (FSERROR) bpreverr1; - errorptr[2] = (FSERROR) bpreverr2; - } -} - - -/* - * Initialize the error-limiting transfer function (lookup table). - * The raw F-S error computation can potentially compute error values of up to - * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be - * much less, otherwise obviously wrong pixels will be created. (Typical - * effects include weird fringes at color-area boundaries, isolated bright - * pixels in a dark area, etc.) The standard advice for avoiding this problem - * is to ensure that the "corners" of the color cube are allocated as output - * colors; then repeated errors in the same direction cannot cause cascading - * error buildup. However, that only prevents the error from getting - * completely out of hand; Aaron Giles reports that error limiting improves - * the results even with corner colors allocated. - * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty - * well, but the smoother transfer function used below is even better. Thanks - * to Aaron Giles for this idea. - */ - -LOCAL(void) -init_error_limit (j_decompress_ptr cinfo) -/* Allocate and fill in the error_limiter table */ -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - int * table; - int in, out; - - table = (int *) (*cinfo->mem->alloc_small) - ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int)); - table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */ - cquantize->error_limiter = table; - -#define STEPSIZE ((MAXJSAMPLE+1)/16) - /* Map errors 1:1 up to +- MAXJSAMPLE/16 */ - out = 0; - for (in = 0; in < STEPSIZE; in++, out++) { - table[in] = out; table[-in] = -out; - } - /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */ - for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) { - table[in] = out; table[-in] = -out; - } - /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */ - for (; in <= MAXJSAMPLE; in++) { - table[in] = out; table[-in] = -out; - } -#undef STEPSIZE -} - - -/* - * Finish up at the end of each pass. - */ - -METHODDEF(void) -finish_pass1 (j_decompress_ptr cinfo) -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - - /* Select the representative colors and fill in cinfo->colormap */ - cinfo->colormap = cquantize->sv_colormap; - select_colors(cinfo, cquantize->desired); - /* Force next pass to zero the color index table */ - cquantize->needs_zeroed = TRUE; -} - - -METHODDEF(void) -finish_pass2 (j_decompress_ptr cinfo) -{ - /* no work */ -} - - -/* - * Initialize for each processing pass. - */ - -METHODDEF(void) -start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan) -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - hist3d histogram = cquantize->histogram; - int i; - - /* Only F-S dithering or no dithering is supported. */ - /* If user asks for ordered dither, give him F-S. */ - if (cinfo->dither_mode != JDITHER_NONE) - cinfo->dither_mode = JDITHER_FS; - - if (is_pre_scan) { - /* Set up method pointers */ - cquantize->pub.color_quantize = prescan_quantize; - cquantize->pub.finish_pass = finish_pass1; - cquantize->needs_zeroed = TRUE; /* Always zero histogram */ - } else { - /* Set up method pointers */ - if (cinfo->dither_mode == JDITHER_FS) - cquantize->pub.color_quantize = pass2_fs_dither; - else - cquantize->pub.color_quantize = pass2_no_dither; - cquantize->pub.finish_pass = finish_pass2; - - /* Make sure color count is acceptable */ - i = cinfo->actual_number_of_colors; - if (i < 1) - ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1); - if (i > MAXNUMCOLORS) - ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); - - if (cinfo->dither_mode == JDITHER_FS) { - size_t arraysize = (size_t) ((cinfo->output_width + 2) * - (3 * SIZEOF(FSERROR))); - /* Allocate Floyd-Steinberg workspace if we didn't already. */ - if (cquantize->fserrors == NULL) - cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) - ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize); - /* Initialize the propagated errors to zero. */ - FMEMZERO((void FAR *) cquantize->fserrors, arraysize); - /* Make the error-limit table if we didn't already. */ - if (cquantize->error_limiter == NULL) - init_error_limit(cinfo); - cquantize->on_odd_row = FALSE; - } - - } - /* Zero the histogram or inverse color map, if necessary */ - if (cquantize->needs_zeroed) { - for (i = 0; i < HIST_C0_ELEMS; i++) { - FMEMZERO((void FAR *) histogram[i], - HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); - } - cquantize->needs_zeroed = FALSE; - } -} - - -/* - * Switch to a new external colormap between output passes. - */ - -METHODDEF(void) -new_color_map_2_quant (j_decompress_ptr cinfo) -{ - my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; - - /* Reset the inverse color map */ - cquantize->needs_zeroed = TRUE; -} - - -/* - * Module initialization routine for 2-pass color quantization. - */ - -GLOBAL(void) -jinit_2pass_quantizer (j_decompress_ptr cinfo) -{ - my_cquantize_ptr cquantize; - int i; - - cquantize = (my_cquantize_ptr) - (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, - SIZEOF(my_cquantizer)); - cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; - cquantize->pub.start_pass = start_pass_2_quant; - cquantize->pub.new_color_map = new_color_map_2_quant; - cquantize->fserrors = NULL; /* flag optional arrays not allocated */ - cquantize->error_limiter = NULL; - - /* Make sure jdmaster didn't give me a case I can't handle */ - if (cinfo->out_color_components != 3) - ERREXIT(cinfo, JERR_NOTIMPL); - - /* Allocate the histogram/inverse colormap storage */ - cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small) - ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d)); - for (i = 0; i < HIST_C0_ELEMS; i++) { - cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large) - ((j_common_ptr) cinfo, JPOOL_IMAGE, - HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); - } - cquantize->needs_zeroed = TRUE; /* histogram is garbage now */ - - /* Allocate storage for the completed colormap, if required. - * We do this now since it is FAR storage and may affect - * the memory manager's space calculations. - */ - if (cinfo->enable_2pass_quant) { - /* Make sure color count is acceptable */ - int desired = cinfo->desired_number_of_colors; - /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */ - if (desired < 8) - ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8); - /* Make sure colormap indexes can be represented by JSAMPLEs */ - if (desired > MAXNUMCOLORS) - ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); - cquantize->sv_colormap = (*cinfo->mem->alloc_sarray) - ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3); - cquantize->desired = desired; - } else - cquantize->sv_colormap = NULL; - - /* Only F-S dithering or no dithering is supported. */ - /* If user asks for ordered dither, give him F-S. */ - if (cinfo->dither_mode != JDITHER_NONE) - cinfo->dither_mode = JDITHER_FS; - - /* Allocate Floyd-Steinberg workspace if necessary. - * This isn't really needed until pass 2, but again it is FAR storage. - * Although we will cope with a later change in dither_mode, - * we do not promise to honor max_memory_to_use if dither_mode changes. - */ - if (cinfo->dither_mode == JDITHER_FS) { - cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) - ((j_common_ptr) cinfo, JPOOL_IMAGE, - (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR)))); - /* Might as well create the error-limiting table too. */ - init_error_limit(cinfo); - } -} - -#endif /* QUANT_2PASS_SUPPORTED */ +/*
+ * jquant2.c
+ *
+ * Copyright (C) 1991-1996, Thomas G. Lane.
+ * Modified 2011 by Guido Vollbeding.
+ * This file is part of the Independent JPEG Group's software.
+ * For conditions of distribution and use, see the accompanying README file.
+ *
+ * This file contains 2-pass color quantization (color mapping) routines.
+ * These routines provide selection of a custom color map for an image,
+ * followed by mapping of the image to that color map, with optional
+ * Floyd-Steinberg dithering.
+ * It is also possible to use just the second pass to map to an arbitrary
+ * externally-given color map.
+ *
+ * Note: ordered dithering is not supported, since there isn't any fast
+ * way to compute intercolor distances; it's unclear that ordered dither's
+ * fundamental assumptions even hold with an irregularly spaced color map.
+ */
+
+#define JPEG_INTERNALS
+#include "jinclude.h"
+#include "jpeglib.h"
+
+#ifdef QUANT_2PASS_SUPPORTED
+
+
+/*
+ * This module implements the well-known Heckbert paradigm for color
+ * quantization. Most of the ideas used here can be traced back to
+ * Heckbert's seminal paper
+ * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
+ * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
+ *
+ * In the first pass over the image, we accumulate a histogram showing the
+ * usage count of each possible color. To keep the histogram to a reasonable
+ * size, we reduce the precision of the input; typical practice is to retain
+ * 5 or 6 bits per color, so that 8 or 4 different input values are counted
+ * in the same histogram cell.
+ *
+ * Next, the color-selection step begins with a box representing the whole
+ * color space, and repeatedly splits the "largest" remaining box until we
+ * have as many boxes as desired colors. Then the mean color in each
+ * remaining box becomes one of the possible output colors.
+ *
+ * The second pass over the image maps each input pixel to the closest output
+ * color (optionally after applying a Floyd-Steinberg dithering correction).
+ * This mapping is logically trivial, but making it go fast enough requires
+ * considerable care.
+ *
+ * Heckbert-style quantizers vary a good deal in their policies for choosing
+ * the "largest" box and deciding where to cut it. The particular policies
+ * used here have proved out well in experimental comparisons, but better ones
+ * may yet be found.
+ *
+ * In earlier versions of the IJG code, this module quantized in YCbCr color
+ * space, processing the raw upsampled data without a color conversion step.
+ * This allowed the color conversion math to be done only once per colormap
+ * entry, not once per pixel. However, that optimization precluded other
+ * useful optimizations (such as merging color conversion with upsampling)
+ * and it also interfered with desired capabilities such as quantizing to an
+ * externally-supplied colormap. We have therefore abandoned that approach.
+ * The present code works in the post-conversion color space, typically RGB.
+ *
+ * To improve the visual quality of the results, we actually work in scaled
+ * RGB space, giving G distances more weight than R, and R in turn more than
+ * B. To do everything in integer math, we must use integer scale factors.
+ * The 2/3/1 scale factors used here correspond loosely to the relative
+ * weights of the colors in the NTSC grayscale equation.
+ * If you want to use this code to quantize a non-RGB color space, you'll
+ * probably need to change these scale factors.
+ */
+
+#define R_SCALE 2 /* scale R distances by this much */
+#define G_SCALE 3 /* scale G distances by this much */
+#define B_SCALE 1 /* and B by this much */
+
+/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
+ * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B
+ * and B,G,R orders. If you define some other weird order in jmorecfg.h,
+ * you'll get compile errors until you extend this logic. In that case
+ * you'll probably want to tweak the histogram sizes too.
+ */
+
+#if RGB_RED == 0
+#define C0_SCALE R_SCALE
+#endif
+#if RGB_BLUE == 0
+#define C0_SCALE B_SCALE
+#endif
+#if RGB_GREEN == 1
+#define C1_SCALE G_SCALE
+#endif
+#if RGB_RED == 2
+#define C2_SCALE R_SCALE
+#endif
+#if RGB_BLUE == 2
+#define C2_SCALE B_SCALE
+#endif
+
+
+/*
+ * First we have the histogram data structure and routines for creating it.
+ *
+ * The number of bits of precision can be adjusted by changing these symbols.
+ * We recommend keeping 6 bits for G and 5 each for R and B.
+ * If you have plenty of memory and cycles, 6 bits all around gives marginally
+ * better results; if you are short of memory, 5 bits all around will save
+ * some space but degrade the results.
+ * To maintain a fully accurate histogram, we'd need to allocate a "long"
+ * (preferably unsigned long) for each cell. In practice this is overkill;
+ * we can get by with 16 bits per cell. Few of the cell counts will overflow,
+ * and clamping those that do overflow to the maximum value will give close-
+ * enough results. This reduces the recommended histogram size from 256Kb
+ * to 128Kb, which is a useful savings on PC-class machines.
+ * (In the second pass the histogram space is re-used for pixel mapping data;
+ * in that capacity, each cell must be able to store zero to the number of
+ * desired colors. 16 bits/cell is plenty for that too.)
+ * Since the JPEG code is intended to run in small memory model on 80x86
+ * machines, we can't just allocate the histogram in one chunk. Instead
+ * of a true 3-D array, we use a row of pointers to 2-D arrays. Each
+ * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
+ * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
+ * on 80x86 machines, the pointer row is in near memory but the actual
+ * arrays are in far memory (same arrangement as we use for image arrays).
+ */
+
+#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
+
+/* These will do the right thing for either R,G,B or B,G,R color order,
+ * but you may not like the results for other color orders.
+ */
+#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
+#define HIST_C1_BITS 6 /* bits of precision in G histogram */
+#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
+
+/* Number of elements along histogram axes. */
+#define HIST_C0_ELEMS (1<<HIST_C0_BITS)
+#define HIST_C1_ELEMS (1<<HIST_C1_BITS)
+#define HIST_C2_ELEMS (1<<HIST_C2_BITS)
+
+/* These are the amounts to shift an input value to get a histogram index. */
+#define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS)
+#define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS)
+#define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS)
+
+
+typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */
+
+typedef histcell FAR * histptr; /* for pointers to histogram cells */
+
+typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
+typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */
+typedef hist2d * hist3d; /* type for top-level pointer */
+
+
+/* Declarations for Floyd-Steinberg dithering.
+ *
+ * Errors are accumulated into the array fserrors[], at a resolution of
+ * 1/16th of a pixel count. The error at a given pixel is propagated
+ * to its not-yet-processed neighbors using the standard F-S fractions,
+ * ... (here) 7/16
+ * 3/16 5/16 1/16
+ * We work left-to-right on even rows, right-to-left on odd rows.
+ *
+ * We can get away with a single array (holding one row's worth of errors)
+ * by using it to store the current row's errors at pixel columns not yet
+ * processed, but the next row's errors at columns already processed. We
+ * need only a few extra variables to hold the errors immediately around the
+ * current column. (If we are lucky, those variables are in registers, but
+ * even if not, they're probably cheaper to access than array elements are.)
+ *
+ * The fserrors[] array has (#columns + 2) entries; the extra entry at
+ * each end saves us from special-casing the first and last pixels.
+ * Each entry is three values long, one value for each color component.
+ *
+ * Note: on a wide image, we might not have enough room in a PC's near data
+ * segment to hold the error array; so it is allocated with alloc_large.
+ */
+
+#if BITS_IN_JSAMPLE == 8
+typedef INT16 FSERROR; /* 16 bits should be enough */
+typedef int LOCFSERROR; /* use 'int' for calculation temps */
+#else
+typedef INT32 FSERROR; /* may need more than 16 bits */
+typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */
+#endif
+
+typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */
+
+
+/* Private subobject */
+
+typedef struct {
+ struct jpeg_color_quantizer pub; /* public fields */
+
+ /* Space for the eventually created colormap is stashed here */
+ JSAMPARRAY sv_colormap; /* colormap allocated at init time */
+ int desired; /* desired # of colors = size of colormap */
+
+ /* Variables for accumulating image statistics */
+ hist3d histogram; /* pointer to the histogram */
+
+ boolean needs_zeroed; /* TRUE if next pass must zero histogram */
+
+ /* Variables for Floyd-Steinberg dithering */
+ FSERRPTR fserrors; /* accumulated errors */
+ boolean on_odd_row; /* flag to remember which row we are on */
+ int * error_limiter; /* table for clamping the applied error */
+} my_cquantizer;
+
+typedef my_cquantizer * my_cquantize_ptr;
+
+
+/*
+ * Prescan some rows of pixels.
+ * In this module the prescan simply updates the histogram, which has been
+ * initialized to zeroes by start_pass.
+ * An output_buf parameter is required by the method signature, but no data
+ * is actually output (in fact the buffer controller is probably passing a
+ * NULL pointer).
+ */
+
+METHODDEF(void)
+prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
+ JSAMPARRAY output_buf, int num_rows)
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ register JSAMPROW ptr;
+ register histptr histp;
+ register hist3d histogram = cquantize->histogram;
+ int row;
+ JDIMENSION col;
+ JDIMENSION width = cinfo->output_width;
+
+ for (row = 0; row < num_rows; row++) {
+ ptr = input_buf[row];
+ for (col = width; col > 0; col--) {
+ /* get pixel value and index into the histogram */
+ histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
+ [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
+ [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
+ /* increment, check for overflow and undo increment if so. */
+ if (++(*histp) <= 0)
+ (*histp)--;
+ ptr += 3;
+ }
+ }
+}
+
+
+/*
+ * Next we have the really interesting routines: selection of a colormap
+ * given the completed histogram.
+ * These routines work with a list of "boxes", each representing a rectangular
+ * subset of the input color space (to histogram precision).
+ */
+
+typedef struct {
+ /* The bounds of the box (inclusive); expressed as histogram indexes */
+ int c0min, c0max;
+ int c1min, c1max;
+ int c2min, c2max;
+ /* The volume (actually 2-norm) of the box */
+ INT32 volume;
+ /* The number of nonzero histogram cells within this box */
+ long colorcount;
+} box;
+
+typedef box * boxptr;
+
+
+LOCAL(boxptr)
+find_biggest_color_pop (boxptr boxlist, int numboxes)
+/* Find the splittable box with the largest color population */
+/* Returns NULL if no splittable boxes remain */
+{
+ register boxptr boxp;
+ register int i;
+ register long maxc = 0;
+ boxptr which = NULL;
+
+ for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
+ if (boxp->colorcount > maxc && boxp->volume > 0) {
+ which = boxp;
+ maxc = boxp->colorcount;
+ }
+ }
+ return which;
+}
+
+
+LOCAL(boxptr)
+find_biggest_volume (boxptr boxlist, int numboxes)
+/* Find the splittable box with the largest (scaled) volume */
+/* Returns NULL if no splittable boxes remain */
+{
+ register boxptr boxp;
+ register int i;
+ register INT32 maxv = 0;
+ boxptr which = NULL;
+
+ for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
+ if (boxp->volume > maxv) {
+ which = boxp;
+ maxv = boxp->volume;
+ }
+ }
+ return which;
+}
+
+
+LOCAL(void)
+update_box (j_decompress_ptr cinfo, boxptr boxp)
+/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
+/* and recompute its volume and population */
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ hist3d histogram = cquantize->histogram;
+ histptr histp;
+ int c0,c1,c2;
+ int c0min,c0max,c1min,c1max,c2min,c2max;
+ INT32 dist0,dist1,dist2;
+ long ccount;
+
+ c0min = boxp->c0min; c0max = boxp->c0max;
+ c1min = boxp->c1min; c1max = boxp->c1max;
+ c2min = boxp->c2min; c2max = boxp->c2max;
+
+ if (c0max > c0min)
+ for (c0 = c0min; c0 <= c0max; c0++)
+ for (c1 = c1min; c1 <= c1max; c1++) {
+ histp = & histogram[c0][c1][c2min];
+ for (c2 = c2min; c2 <= c2max; c2++)
+ if (*histp++ != 0) {
+ boxp->c0min = c0min = c0;
+ goto have_c0min;
+ }
+ }
+ have_c0min:
+ if (c0max > c0min)
+ for (c0 = c0max; c0 >= c0min; c0--)
+ for (c1 = c1min; c1 <= c1max; c1++) {
+ histp = & histogram[c0][c1][c2min];
+ for (c2 = c2min; c2 <= c2max; c2++)
+ if (*histp++ != 0) {
+ boxp->c0max = c0max = c0;
+ goto have_c0max;
+ }
+ }
+ have_c0max:
+ if (c1max > c1min)
+ for (c1 = c1min; c1 <= c1max; c1++)
+ for (c0 = c0min; c0 <= c0max; c0++) {
+ histp = & histogram[c0][c1][c2min];
+ for (c2 = c2min; c2 <= c2max; c2++)
+ if (*histp++ != 0) {
+ boxp->c1min = c1min = c1;
+ goto have_c1min;
+ }
+ }
+ have_c1min:
+ if (c1max > c1min)
+ for (c1 = c1max; c1 >= c1min; c1--)
+ for (c0 = c0min; c0 <= c0max; c0++) {
+ histp = & histogram[c0][c1][c2min];
+ for (c2 = c2min; c2 <= c2max; c2++)
+ if (*histp++ != 0) {
+ boxp->c1max = c1max = c1;
+ goto have_c1max;
+ }
+ }
+ have_c1max:
+ if (c2max > c2min)
+ for (c2 = c2min; c2 <= c2max; c2++)
+ for (c0 = c0min; c0 <= c0max; c0++) {
+ histp = & histogram[c0][c1min][c2];
+ for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
+ if (*histp != 0) {
+ boxp->c2min = c2min = c2;
+ goto have_c2min;
+ }
+ }
+ have_c2min:
+ if (c2max > c2min)
+ for (c2 = c2max; c2 >= c2min; c2--)
+ for (c0 = c0min; c0 <= c0max; c0++) {
+ histp = & histogram[c0][c1min][c2];
+ for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
+ if (*histp != 0) {
+ boxp->c2max = c2max = c2;
+ goto have_c2max;
+ }
+ }
+ have_c2max:
+
+ /* Update box volume.
+ * We use 2-norm rather than real volume here; this biases the method
+ * against making long narrow boxes, and it has the side benefit that
+ * a box is splittable iff norm > 0.
+ * Since the differences are expressed in histogram-cell units,
+ * we have to shift back to JSAMPLE units to get consistent distances;
+ * after which, we scale according to the selected distance scale factors.
+ */
+ dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
+ dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
+ dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
+ boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
+
+ /* Now scan remaining volume of box and compute population */
+ ccount = 0;
+ for (c0 = c0min; c0 <= c0max; c0++)
+ for (c1 = c1min; c1 <= c1max; c1++) {
+ histp = & histogram[c0][c1][c2min];
+ for (c2 = c2min; c2 <= c2max; c2++, histp++)
+ if (*histp != 0) {
+ ccount++;
+ }
+ }
+ boxp->colorcount = ccount;
+}
+
+
+LOCAL(int)
+median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
+ int desired_colors)
+/* Repeatedly select and split the largest box until we have enough boxes */
+{
+ int n,lb;
+ int c0,c1,c2,cmax;
+ register boxptr b1,b2;
+
+ while (numboxes < desired_colors) {
+ /* Select box to split.
+ * Current algorithm: by population for first half, then by volume.
+ */
+ if (numboxes*2 <= desired_colors) {
+ b1 = find_biggest_color_pop(boxlist, numboxes);
+ } else {
+ b1 = find_biggest_volume(boxlist, numboxes);
+ }
+ if (b1 == NULL) /* no splittable boxes left! */
+ break;
+ b2 = &boxlist[numboxes]; /* where new box will go */
+ /* Copy the color bounds to the new box. */
+ b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
+ b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
+ /* Choose which axis to split the box on.
+ * Current algorithm: longest scaled axis.
+ * See notes in update_box about scaling distances.
+ */
+ c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
+ c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
+ c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
+ /* We want to break any ties in favor of green, then red, blue last.
+ * This code does the right thing for R,G,B or B,G,R color orders only.
+ */
+#if RGB_RED == 0
+ cmax = c1; n = 1;
+ if (c0 > cmax) { cmax = c0; n = 0; }
+ if (c2 > cmax) { n = 2; }
+#else
+ cmax = c1; n = 1;
+ if (c2 > cmax) { cmax = c2; n = 2; }
+ if (c0 > cmax) { n = 0; }
+#endif
+ /* Choose split point along selected axis, and update box bounds.
+ * Current algorithm: split at halfway point.
+ * (Since the box has been shrunk to minimum volume,
+ * any split will produce two nonempty subboxes.)
+ * Note that lb value is max for lower box, so must be < old max.
+ */
+ switch (n) {
+ case 0:
+ lb = (b1->c0max + b1->c0min) / 2;
+ b1->c0max = lb;
+ b2->c0min = lb+1;
+ break;
+ case 1:
+ lb = (b1->c1max + b1->c1min) / 2;
+ b1->c1max = lb;
+ b2->c1min = lb+1;
+ break;
+ case 2:
+ lb = (b1->c2max + b1->c2min) / 2;
+ b1->c2max = lb;
+ b2->c2min = lb+1;
+ break;
+ }
+ /* Update stats for boxes */
+ update_box(cinfo, b1);
+ update_box(cinfo, b2);
+ numboxes++;
+ }
+ return numboxes;
+}
+
+
+LOCAL(void)
+compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
+/* Compute representative color for a box, put it in colormap[icolor] */
+{
+ /* Current algorithm: mean weighted by pixels (not colors) */
+ /* Note it is important to get the rounding correct! */
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ hist3d histogram = cquantize->histogram;
+ histptr histp;
+ int c0,c1,c2;
+ int c0min,c0max,c1min,c1max,c2min,c2max;
+ long count;
+ long total = 0;
+ long c0total = 0;
+ long c1total = 0;
+ long c2total = 0;
+
+ c0min = boxp->c0min; c0max = boxp->c0max;
+ c1min = boxp->c1min; c1max = boxp->c1max;
+ c2min = boxp->c2min; c2max = boxp->c2max;
+
+ for (c0 = c0min; c0 <= c0max; c0++)
+ for (c1 = c1min; c1 <= c1max; c1++) {
+ histp = & histogram[c0][c1][c2min];
+ for (c2 = c2min; c2 <= c2max; c2++) {
+ if ((count = *histp++) != 0) {
+ total += count;
+ c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
+ c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
+ c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
+ }
+ }
+ }
+
+ cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
+ cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
+ cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
+}
+
+
+LOCAL(void)
+select_colors (j_decompress_ptr cinfo, int desired_colors)
+/* Master routine for color selection */
+{
+ boxptr boxlist;
+ int numboxes;
+ int i;
+
+ /* Allocate workspace for box list */
+ boxlist = (boxptr) (*cinfo->mem->alloc_small)
+ ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
+ /* Initialize one box containing whole space */
+ numboxes = 1;
+ boxlist[0].c0min = 0;
+ boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
+ boxlist[0].c1min = 0;
+ boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
+ boxlist[0].c2min = 0;
+ boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
+ /* Shrink it to actually-used volume and set its statistics */
+ update_box(cinfo, & boxlist[0]);
+ /* Perform median-cut to produce final box list */
+ numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
+ /* Compute the representative color for each box, fill colormap */
+ for (i = 0; i < numboxes; i++)
+ compute_color(cinfo, & boxlist[i], i);
+ cinfo->actual_number_of_colors = numboxes;
+ TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
+}
+
+
+/*
+ * These routines are concerned with the time-critical task of mapping input
+ * colors to the nearest color in the selected colormap.
+ *
+ * We re-use the histogram space as an "inverse color map", essentially a
+ * cache for the results of nearest-color searches. All colors within a
+ * histogram cell will be mapped to the same colormap entry, namely the one
+ * closest to the cell's center. This may not be quite the closest entry to
+ * the actual input color, but it's almost as good. A zero in the cache
+ * indicates we haven't found the nearest color for that cell yet; the array
+ * is cleared to zeroes before starting the mapping pass. When we find the
+ * nearest color for a cell, its colormap index plus one is recorded in the
+ * cache for future use. The pass2 scanning routines call fill_inverse_cmap
+ * when they need to use an unfilled entry in the cache.
+ *
+ * Our method of efficiently finding nearest colors is based on the "locally
+ * sorted search" idea described by Heckbert and on the incremental distance
+ * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
+ * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
+ * the distances from a given colormap entry to each cell of the histogram can
+ * be computed quickly using an incremental method: the differences between
+ * distances to adjacent cells themselves differ by a constant. This allows a
+ * fairly fast implementation of the "brute force" approach of computing the
+ * distance from every colormap entry to every histogram cell. Unfortunately,
+ * it needs a work array to hold the best-distance-so-far for each histogram
+ * cell (because the inner loop has to be over cells, not colormap entries).
+ * The work array elements have to be INT32s, so the work array would need
+ * 256Kb at our recommended precision. This is not feasible in DOS machines.
+ *
+ * To get around these problems, we apply Thomas' method to compute the
+ * nearest colors for only the cells within a small subbox of the histogram.
+ * The work array need be only as big as the subbox, so the memory usage
+ * problem is solved. Furthermore, we need not fill subboxes that are never
+ * referenced in pass2; many images use only part of the color gamut, so a
+ * fair amount of work is saved. An additional advantage of this
+ * approach is that we can apply Heckbert's locality criterion to quickly
+ * eliminate colormap entries that are far away from the subbox; typically
+ * three-fourths of the colormap entries are rejected by Heckbert's criterion,
+ * and we need not compute their distances to individual cells in the subbox.
+ * The speed of this approach is heavily influenced by the subbox size: too
+ * small means too much overhead, too big loses because Heckbert's criterion
+ * can't eliminate as many colormap entries. Empirically the best subbox
+ * size seems to be about 1/512th of the histogram (1/8th in each direction).
+ *
+ * Thomas' article also describes a refined method which is asymptotically
+ * faster than the brute-force method, but it is also far more complex and
+ * cannot efficiently be applied to small subboxes. It is therefore not
+ * useful for programs intended to be portable to DOS machines. On machines
+ * with plenty of memory, filling the whole histogram in one shot with Thomas'
+ * refined method might be faster than the present code --- but then again,
+ * it might not be any faster, and it's certainly more complicated.
+ */
+
+
+/* log2(histogram cells in update box) for each axis; this can be adjusted */
+#define BOX_C0_LOG (HIST_C0_BITS-3)
+#define BOX_C1_LOG (HIST_C1_BITS-3)
+#define BOX_C2_LOG (HIST_C2_BITS-3)
+
+#define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */
+#define BOX_C1_ELEMS (1<<BOX_C1_LOG)
+#define BOX_C2_ELEMS (1<<BOX_C2_LOG)
+
+#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
+#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
+#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)
+
+
+/*
+ * The next three routines implement inverse colormap filling. They could
+ * all be folded into one big routine, but splitting them up this way saves
+ * some stack space (the mindist[] and bestdist[] arrays need not coexist)
+ * and may allow some compilers to produce better code by registerizing more
+ * inner-loop variables.
+ */
+
+LOCAL(int)
+find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
+ JSAMPLE colorlist[])
+/* Locate the colormap entries close enough to an update box to be candidates
+ * for the nearest entry to some cell(s) in the update box. The update box
+ * is specified by the center coordinates of its first cell. The number of
+ * candidate colormap entries is returned, and their colormap indexes are
+ * placed in colorlist[].
+ * This routine uses Heckbert's "locally sorted search" criterion to select
+ * the colors that need further consideration.
+ */
+{
+ int numcolors = cinfo->actual_number_of_colors;
+ int maxc0, maxc1, maxc2;
+ int centerc0, centerc1, centerc2;
+ int i, x, ncolors;
+ INT32 minmaxdist, min_dist, max_dist, tdist;
+ INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
+
+ /* Compute true coordinates of update box's upper corner and center.
+ * Actually we compute the coordinates of the center of the upper-corner
+ * histogram cell, which are the upper bounds of the volume we care about.
+ * Note that since ">>" rounds down, the "center" values may be closer to
+ * min than to max; hence comparisons to them must be "<=", not "<".
+ */
+ maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
+ centerc0 = (minc0 + maxc0) >> 1;
+ maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
+ centerc1 = (minc1 + maxc1) >> 1;
+ maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
+ centerc2 = (minc2 + maxc2) >> 1;
+
+ /* For each color in colormap, find:
+ * 1. its minimum squared-distance to any point in the update box
+ * (zero if color is within update box);
+ * 2. its maximum squared-distance to any point in the update box.
+ * Both of these can be found by considering only the corners of the box.
+ * We save the minimum distance for each color in mindist[];
+ * only the smallest maximum distance is of interest.
+ */
+ minmaxdist = 0x7FFFFFFFL;
+
+ for (i = 0; i < numcolors; i++) {
+ /* We compute the squared-c0-distance term, then add in the other two. */
+ x = GETJSAMPLE(cinfo->colormap[0][i]);
+ if (x < minc0) {
+ tdist = (x - minc0) * C0_SCALE;
+ min_dist = tdist*tdist;
+ tdist = (x - maxc0) * C0_SCALE;
+ max_dist = tdist*tdist;
+ } else if (x > maxc0) {
+ tdist = (x - maxc0) * C0_SCALE;
+ min_dist = tdist*tdist;
+ tdist = (x - minc0) * C0_SCALE;
+ max_dist = tdist*tdist;
+ } else {
+ /* within cell range so no contribution to min_dist */
+ min_dist = 0;
+ if (x <= centerc0) {
+ tdist = (x - maxc0) * C0_SCALE;
+ max_dist = tdist*tdist;
+ } else {
+ tdist = (x - minc0) * C0_SCALE;
+ max_dist = tdist*tdist;
+ }
+ }
+
+ x = GETJSAMPLE(cinfo->colormap[1][i]);
+ if (x < minc1) {
+ tdist = (x - minc1) * C1_SCALE;
+ min_dist += tdist*tdist;
+ tdist = (x - maxc1) * C1_SCALE;
+ max_dist += tdist*tdist;
+ } else if (x > maxc1) {
+ tdist = (x - maxc1) * C1_SCALE;
+ min_dist += tdist*tdist;
+ tdist = (x - minc1) * C1_SCALE;
+ max_dist += tdist*tdist;
+ } else {
+ /* within cell range so no contribution to min_dist */
+ if (x <= centerc1) {
+ tdist = (x - maxc1) * C1_SCALE;
+ max_dist += tdist*tdist;
+ } else {
+ tdist = (x - minc1) * C1_SCALE;
+ max_dist += tdist*tdist;
+ }
+ }
+
+ x = GETJSAMPLE(cinfo->colormap[2][i]);
+ if (x < minc2) {
+ tdist = (x - minc2) * C2_SCALE;
+ min_dist += tdist*tdist;
+ tdist = (x - maxc2) * C2_SCALE;
+ max_dist += tdist*tdist;
+ } else if (x > maxc2) {
+ tdist = (x - maxc2) * C2_SCALE;
+ min_dist += tdist*tdist;
+ tdist = (x - minc2) * C2_SCALE;
+ max_dist += tdist*tdist;
+ } else {
+ /* within cell range so no contribution to min_dist */
+ if (x <= centerc2) {
+ tdist = (x - maxc2) * C2_SCALE;
+ max_dist += tdist*tdist;
+ } else {
+ tdist = (x - minc2) * C2_SCALE;
+ max_dist += tdist*tdist;
+ }
+ }
+
+ mindist[i] = min_dist; /* save away the results */
+ if (max_dist < minmaxdist)
+ minmaxdist = max_dist;
+ }
+
+ /* Now we know that no cell in the update box is more than minmaxdist
+ * away from some colormap entry. Therefore, only colors that are
+ * within minmaxdist of some part of the box need be considered.
+ */
+ ncolors = 0;
+ for (i = 0; i < numcolors; i++) {
+ if (mindist[i] <= minmaxdist)
+ colorlist[ncolors++] = (JSAMPLE) i;
+ }
+ return ncolors;
+}
+
+
+LOCAL(void)
+find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
+ int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
+/* Find the closest colormap entry for each cell in the update box,
+ * given the list of candidate colors prepared by find_nearby_colors.
+ * Return the indexes of the closest entries in the bestcolor[] array.
+ * This routine uses Thomas' incremental distance calculation method to
+ * find the distance from a colormap entry to successive cells in the box.
+ */
+{
+ int ic0, ic1, ic2;
+ int i, icolor;
+ register INT32 * bptr; /* pointer into bestdist[] array */
+ JSAMPLE * cptr; /* pointer into bestcolor[] array */
+ INT32 dist0, dist1; /* initial distance values */
+ register INT32 dist2; /* current distance in inner loop */
+ INT32 xx0, xx1; /* distance increments */
+ register INT32 xx2;
+ INT32 inc0, inc1, inc2; /* initial values for increments */
+ /* This array holds the distance to the nearest-so-far color for each cell */
+ INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
+
+ /* Initialize best-distance for each cell of the update box */
+ bptr = bestdist;
+ for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
+ *bptr++ = 0x7FFFFFFFL;
+
+ /* For each color selected by find_nearby_colors,
+ * compute its distance to the center of each cell in the box.
+ * If that's less than best-so-far, update best distance and color number.
+ */
+
+ /* Nominal steps between cell centers ("x" in Thomas article) */
+#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
+#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
+#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
+
+ for (i = 0; i < numcolors; i++) {
+ icolor = GETJSAMPLE(colorlist[i]);
+ /* Compute (square of) distance from minc0/c1/c2 to this color */
+ inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
+ dist0 = inc0*inc0;
+ inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
+ dist0 += inc1*inc1;
+ inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
+ dist0 += inc2*inc2;
+ /* Form the initial difference increments */
+ inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
+ inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
+ inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
+ /* Now loop over all cells in box, updating distance per Thomas method */
+ bptr = bestdist;
+ cptr = bestcolor;
+ xx0 = inc0;
+ for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
+ dist1 = dist0;
+ xx1 = inc1;
+ for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
+ dist2 = dist1;
+ xx2 = inc2;
+ for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
+ if (dist2 < *bptr) {
+ *bptr = dist2;
+ *cptr = (JSAMPLE) icolor;
+ }
+ dist2 += xx2;
+ xx2 += 2 * STEP_C2 * STEP_C2;
+ bptr++;
+ cptr++;
+ }
+ dist1 += xx1;
+ xx1 += 2 * STEP_C1 * STEP_C1;
+ }
+ dist0 += xx0;
+ xx0 += 2 * STEP_C0 * STEP_C0;
+ }
+ }
+}
+
+
+LOCAL(void)
+fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
+/* Fill the inverse-colormap entries in the update box that contains */
+/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
+/* we can fill as many others as we wish.) */
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ hist3d histogram = cquantize->histogram;
+ int minc0, minc1, minc2; /* lower left corner of update box */
+ int ic0, ic1, ic2;
+ register JSAMPLE * cptr; /* pointer into bestcolor[] array */
+ register histptr cachep; /* pointer into main cache array */
+ /* This array lists the candidate colormap indexes. */
+ JSAMPLE colorlist[MAXNUMCOLORS];
+ int numcolors; /* number of candidate colors */
+ /* This array holds the actually closest colormap index for each cell. */
+ JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
+
+ /* Convert cell coordinates to update box ID */
+ c0 >>= BOX_C0_LOG;
+ c1 >>= BOX_C1_LOG;
+ c2 >>= BOX_C2_LOG;
+
+ /* Compute true coordinates of update box's origin corner.
+ * Actually we compute the coordinates of the center of the corner
+ * histogram cell, which are the lower bounds of the volume we care about.
+ */
+ minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
+ minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
+ minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
+
+ /* Determine which colormap entries are close enough to be candidates
+ * for the nearest entry to some cell in the update box.
+ */
+ numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
+
+ /* Determine the actually nearest colors. */
+ find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
+ bestcolor);
+
+ /* Save the best color numbers (plus 1) in the main cache array */
+ c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
+ c1 <<= BOX_C1_LOG;
+ c2 <<= BOX_C2_LOG;
+ cptr = bestcolor;
+ for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
+ for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
+ cachep = & histogram[c0+ic0][c1+ic1][c2];
+ for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
+ *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
+ }
+ }
+ }
+}
+
+
+/*
+ * Map some rows of pixels to the output colormapped representation.
+ */
+
+METHODDEF(void)
+pass2_no_dither (j_decompress_ptr cinfo,
+ JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
+/* This version performs no dithering */
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ hist3d histogram = cquantize->histogram;
+ register JSAMPROW inptr, outptr;
+ register histptr cachep;
+ register int c0, c1, c2;
+ int row;
+ JDIMENSION col;
+ JDIMENSION width = cinfo->output_width;
+
+ for (row = 0; row < num_rows; row++) {
+ inptr = input_buf[row];
+ outptr = output_buf[row];
+ for (col = width; col > 0; col--) {
+ /* get pixel value and index into the cache */
+ c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
+ c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
+ c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
+ cachep = & histogram[c0][c1][c2];
+ /* If we have not seen this color before, find nearest colormap entry */
+ /* and update the cache */
+ if (*cachep == 0)
+ fill_inverse_cmap(cinfo, c0,c1,c2);
+ /* Now emit the colormap index for this cell */
+ *outptr++ = (JSAMPLE) (*cachep - 1);
+ }
+ }
+}
+
+
+METHODDEF(void)
+pass2_fs_dither (j_decompress_ptr cinfo,
+ JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
+/* This version performs Floyd-Steinberg dithering */
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ hist3d histogram = cquantize->histogram;
+ register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
+ LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
+ LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
+ register FSERRPTR errorptr; /* => fserrors[] at column before current */
+ JSAMPROW inptr; /* => current input pixel */
+ JSAMPROW outptr; /* => current output pixel */
+ histptr cachep;
+ int dir; /* +1 or -1 depending on direction */
+ int dir3; /* 3*dir, for advancing inptr & errorptr */
+ int row;
+ JDIMENSION col;
+ JDIMENSION width = cinfo->output_width;
+ JSAMPLE *range_limit = cinfo->sample_range_limit;
+ int *error_limit = cquantize->error_limiter;
+ JSAMPROW colormap0 = cinfo->colormap[0];
+ JSAMPROW colormap1 = cinfo->colormap[1];
+ JSAMPROW colormap2 = cinfo->colormap[2];
+ SHIFT_TEMPS
+
+ for (row = 0; row < num_rows; row++) {
+ inptr = input_buf[row];
+ outptr = output_buf[row];
+ if (cquantize->on_odd_row) {
+ /* work right to left in this row */
+ inptr += (width-1) * 3; /* so point to rightmost pixel */
+ outptr += width-1;
+ dir = -1;
+ dir3 = -3;
+ errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
+ cquantize->on_odd_row = FALSE; /* flip for next time */
+ } else {
+ /* work left to right in this row */
+ dir = 1;
+ dir3 = 3;
+ errorptr = cquantize->fserrors; /* => entry before first real column */
+ cquantize->on_odd_row = TRUE; /* flip for next time */
+ }
+ /* Preset error values: no error propagated to first pixel from left */
+ cur0 = cur1 = cur2 = 0;
+ /* and no error propagated to row below yet */
+ belowerr0 = belowerr1 = belowerr2 = 0;
+ bpreverr0 = bpreverr1 = bpreverr2 = 0;
+
+ for (col = width; col > 0; col--) {
+ /* curN holds the error propagated from the previous pixel on the
+ * current line. Add the error propagated from the previous line
+ * to form the complete error correction term for this pixel, and
+ * round the error term (which is expressed * 16) to an integer.
+ * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
+ * for either sign of the error value.
+ * Note: errorptr points to *previous* column's array entry.
+ */
+ cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
+ cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
+ cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
+ /* Limit the error using transfer function set by init_error_limit.
+ * See comments with init_error_limit for rationale.
+ */
+ cur0 = error_limit[cur0];
+ cur1 = error_limit[cur1];
+ cur2 = error_limit[cur2];
+ /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
+ * The maximum error is +- MAXJSAMPLE (or less with error limiting);
+ * this sets the required size of the range_limit array.
+ */
+ cur0 += GETJSAMPLE(inptr[0]);
+ cur1 += GETJSAMPLE(inptr[1]);
+ cur2 += GETJSAMPLE(inptr[2]);
+ cur0 = GETJSAMPLE(range_limit[cur0]);
+ cur1 = GETJSAMPLE(range_limit[cur1]);
+ cur2 = GETJSAMPLE(range_limit[cur2]);
+ /* Index into the cache with adjusted pixel value */
+ cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
+ /* If we have not seen this color before, find nearest colormap */
+ /* entry and update the cache */
+ if (*cachep == 0)
+ fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
+ /* Now emit the colormap index for this cell */
+ { register int pixcode = *cachep - 1;
+ *outptr = (JSAMPLE) pixcode;
+ /* Compute representation error for this pixel */
+ cur0 -= GETJSAMPLE(colormap0[pixcode]);
+ cur1 -= GETJSAMPLE(colormap1[pixcode]);
+ cur2 -= GETJSAMPLE(colormap2[pixcode]);
+ }
+ /* Compute error fractions to be propagated to adjacent pixels.
+ * Add these into the running sums, and simultaneously shift the
+ * next-line error sums left by 1 column.
+ */
+ { register LOCFSERROR bnexterr, delta;
+
+ bnexterr = cur0; /* Process component 0 */
+ delta = cur0 * 2;
+ cur0 += delta; /* form error * 3 */
+ errorptr[0] = (FSERROR) (bpreverr0 + cur0);
+ cur0 += delta; /* form error * 5 */
+ bpreverr0 = belowerr0 + cur0;
+ belowerr0 = bnexterr;
+ cur0 += delta; /* form error * 7 */
+ bnexterr = cur1; /* Process component 1 */
+ delta = cur1 * 2;
+ cur1 += delta; /* form error * 3 */
+ errorptr[1] = (FSERROR) (bpreverr1 + cur1);
+ cur1 += delta; /* form error * 5 */
+ bpreverr1 = belowerr1 + cur1;
+ belowerr1 = bnexterr;
+ cur1 += delta; /* form error * 7 */
+ bnexterr = cur2; /* Process component 2 */
+ delta = cur2 * 2;
+ cur2 += delta; /* form error * 3 */
+ errorptr[2] = (FSERROR) (bpreverr2 + cur2);
+ cur2 += delta; /* form error * 5 */
+ bpreverr2 = belowerr2 + cur2;
+ belowerr2 = bnexterr;
+ cur2 += delta; /* form error * 7 */
+ }
+ /* At this point curN contains the 7/16 error value to be propagated
+ * to the next pixel on the current line, and all the errors for the
+ * next line have been shifted over. We are therefore ready to move on.
+ */
+ inptr += dir3; /* Advance pixel pointers to next column */
+ outptr += dir;
+ errorptr += dir3; /* advance errorptr to current column */
+ }
+ /* Post-loop cleanup: we must unload the final error values into the
+ * final fserrors[] entry. Note we need not unload belowerrN because
+ * it is for the dummy column before or after the actual array.
+ */
+ errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
+ errorptr[1] = (FSERROR) bpreverr1;
+ errorptr[2] = (FSERROR) bpreverr2;
+ }
+}
+
+
+/*
+ * Initialize the error-limiting transfer function (lookup table).
+ * The raw F-S error computation can potentially compute error values of up to
+ * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
+ * much less, otherwise obviously wrong pixels will be created. (Typical
+ * effects include weird fringes at color-area boundaries, isolated bright
+ * pixels in a dark area, etc.) The standard advice for avoiding this problem
+ * is to ensure that the "corners" of the color cube are allocated as output
+ * colors; then repeated errors in the same direction cannot cause cascading
+ * error buildup. However, that only prevents the error from getting
+ * completely out of hand; Aaron Giles reports that error limiting improves
+ * the results even with corner colors allocated.
+ * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
+ * well, but the smoother transfer function used below is even better. Thanks
+ * to Aaron Giles for this idea.
+ */
+
+LOCAL(void)
+init_error_limit (j_decompress_ptr cinfo)
+/* Allocate and fill in the error_limiter table */
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ int * table;
+ int in, out;
+
+ table = (int *) (*cinfo->mem->alloc_small)
+ ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
+ table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
+ cquantize->error_limiter = table;
+
+#define STEPSIZE ((MAXJSAMPLE+1)/16)
+ /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
+ out = 0;
+ for (in = 0; in < STEPSIZE; in++, out++) {
+ table[in] = out; table[-in] = -out;
+ }
+ /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
+ for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
+ table[in] = out; table[-in] = -out;
+ }
+ /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
+ for (; in <= MAXJSAMPLE; in++) {
+ table[in] = out; table[-in] = -out;
+ }
+#undef STEPSIZE
+}
+
+
+/*
+ * Finish up at the end of each pass.
+ */
+
+METHODDEF(void)
+finish_pass1 (j_decompress_ptr cinfo)
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+
+ /* Select the representative colors and fill in cinfo->colormap */
+ cinfo->colormap = cquantize->sv_colormap;
+ select_colors(cinfo, cquantize->desired);
+ /* Force next pass to zero the color index table */
+ cquantize->needs_zeroed = TRUE;
+}
+
+
+METHODDEF(void)
+finish_pass2 (j_decompress_ptr cinfo)
+{
+ /* no work */
+}
+
+
+/*
+ * Initialize for each processing pass.
+ */
+
+METHODDEF(void)
+start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+ hist3d histogram = cquantize->histogram;
+ int i;
+
+ /* Only F-S dithering or no dithering is supported. */
+ /* If user asks for ordered dither, give him F-S. */
+ if (cinfo->dither_mode != JDITHER_NONE)
+ cinfo->dither_mode = JDITHER_FS;
+
+ if (is_pre_scan) {
+ /* Set up method pointers */
+ cquantize->pub.color_quantize = prescan_quantize;
+ cquantize->pub.finish_pass = finish_pass1;
+ cquantize->needs_zeroed = TRUE; /* Always zero histogram */
+ } else {
+ /* Set up method pointers */
+ if (cinfo->dither_mode == JDITHER_FS)
+ cquantize->pub.color_quantize = pass2_fs_dither;
+ else
+ cquantize->pub.color_quantize = pass2_no_dither;
+ cquantize->pub.finish_pass = finish_pass2;
+
+ /* Make sure color count is acceptable */
+ i = cinfo->actual_number_of_colors;
+ if (i < 1)
+ ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
+ if (i > MAXNUMCOLORS)
+ ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
+
+ if (cinfo->dither_mode == JDITHER_FS) {
+ size_t arraysize = (size_t) ((cinfo->output_width + 2) *
+ (3 * SIZEOF(FSERROR)));
+ /* Allocate Floyd-Steinberg workspace if we didn't already. */
+ if (cquantize->fserrors == NULL)
+ cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
+ ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
+ /* Initialize the propagated errors to zero. */
+ FMEMZERO((void FAR *) cquantize->fserrors, arraysize);
+ /* Make the error-limit table if we didn't already. */
+ if (cquantize->error_limiter == NULL)
+ init_error_limit(cinfo);
+ cquantize->on_odd_row = FALSE;
+ }
+
+ }
+ /* Zero the histogram or inverse color map, if necessary */
+ if (cquantize->needs_zeroed) {
+ for (i = 0; i < HIST_C0_ELEMS; i++) {
+ FMEMZERO((void FAR *) histogram[i],
+ HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
+ }
+ cquantize->needs_zeroed = FALSE;
+ }
+}
+
+
+/*
+ * Switch to a new external colormap between output passes.
+ */
+
+METHODDEF(void)
+new_color_map_2_quant (j_decompress_ptr cinfo)
+{
+ my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+
+ /* Reset the inverse color map */
+ cquantize->needs_zeroed = TRUE;
+}
+
+
+/*
+ * Module initialization routine for 2-pass color quantization.
+ */
+
+GLOBAL(void)
+jinit_2pass_quantizer (j_decompress_ptr cinfo)
+{
+ my_cquantize_ptr cquantize;
+ int i;
+
+ cquantize = (my_cquantize_ptr)
+ (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
+ SIZEOF(my_cquantizer));
+ cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
+ cquantize->pub.start_pass = start_pass_2_quant;
+ cquantize->pub.new_color_map = new_color_map_2_quant;
+ cquantize->fserrors = NULL; /* flag optional arrays not allocated */
+ cquantize->error_limiter = NULL;
+
+ /* Make sure jdmaster didn't give me a case I can't handle */
+ if (cinfo->out_color_components != 3)
+ ERREXIT(cinfo, JERR_NOTIMPL);
+
+ /* Allocate the histogram/inverse colormap storage */
+ cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
+ ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
+ for (i = 0; i < HIST_C0_ELEMS; i++) {
+ cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
+ ((j_common_ptr) cinfo, JPOOL_IMAGE,
+ HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
+ }
+ cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
+
+ /* Allocate storage for the completed colormap, if required.
+ * We do this now since it is FAR storage and may affect
+ * the memory manager's space calculations.
+ */
+ if (cinfo->enable_2pass_quant) {
+ /* Make sure color count is acceptable */
+ int desired = cinfo->desired_number_of_colors;
+ /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
+ if (desired < 8)
+ ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
+ /* Make sure colormap indexes can be represented by JSAMPLEs */
+ if (desired > MAXNUMCOLORS)
+ ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
+ cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
+ ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
+ cquantize->desired = desired;
+ } else
+ cquantize->sv_colormap = NULL;
+
+ /* Only F-S dithering or no dithering is supported. */
+ /* If user asks for ordered dither, give him F-S. */
+ if (cinfo->dither_mode != JDITHER_NONE)
+ cinfo->dither_mode = JDITHER_FS;
+
+ /* Allocate Floyd-Steinberg workspace if necessary.
+ * This isn't really needed until pass 2, but again it is FAR storage.
+ * Although we will cope with a later change in dither_mode,
+ * we do not promise to honor max_memory_to_use if dither_mode changes.
+ */
+ if (cinfo->dither_mode == JDITHER_FS) {
+ cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
+ ((j_common_ptr) cinfo, JPOOL_IMAGE,
+ (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
+ /* Might as well create the error-limiting table too. */
+ init_error_limit(cinfo);
+ }
+}
+
+#endif /* QUANT_2PASS_SUPPORTED */
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