+++ /dev/null
-/*
- * jquant2.c
- *
- * Copyright (C) 1991-1996, Thomas G. Lane.
- * 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. */
- jzero_far((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++) {
- jzero_far((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 */
projects / opencv / blobdiff