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Diffstat (limited to 'media/libjpeg/jquant2.c')
| -rw-r--r-- | media/libjpeg/jquant2.c | 1285 |
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diff --git a/media/libjpeg/jquant2.c b/media/libjpeg/jquant2.c new file mode 100644 index 0000000000..44efb18cad --- /dev/null +++ b/media/libjpeg/jquant2.c @@ -0,0 +1,1285 @@ +/* + * jquant2.c + * + * This file was part of the Independent JPEG Group's software: + * Copyright (C) 1991-1996, Thomas G. Lane. + * libjpeg-turbo Modifications: + * Copyright (C) 2009, 2014-2015, 2020, D. R. Commander. + * For conditions of distribution and use, see the accompanying README.ijg + * 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 */ + +static const int c_scales[3] = { R_SCALE, G_SCALE, B_SCALE }; +#define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]] +#define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]] +#define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]] + +/* + * 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. + */ + +#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 *histptr; /* for pointers to histogram cells */ + +typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */ +typedef hist1d *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. + */ + +#if BITS_IN_JSAMPLE == 8 +typedef INT16 FSERROR; /* 16 bits should be enough */ +typedef int LOCFSERROR; /* use 'int' for calculation temps */ +#else +typedef JLONG FSERROR; /* may need more than 16 bits */ +typedef JLONG LOCFSERROR; /* be sure calculation temps are big enough */ +#endif + +typedef FSERROR *FSERRPTR; /* pointer to error array */ + + +/* 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[ptr[0] >> C0_SHIFT] + [ptr[1] >> C1_SHIFT] + [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 */ + JLONG 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 JLONG 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; + JLONG 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[cinfo->out_color_space] == 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; } + } + /* 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 JLONGs, 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; + JLONG minmaxdist, min_dist, max_dist, tdist; + JLONG 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 = 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 = 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 = 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 JLONG *bptr; /* pointer into bestdist[] array */ + JSAMPLE *cptr; /* pointer into bestcolor[] array */ + JLONG dist0, dist1; /* initial distance values */ + register JLONG dist2; /* current distance in inner loop */ + JLONG xx0, xx1; /* distance increments */ + register JLONG xx2; + JLONG inc0, inc1, inc2; /* initial values for increments */ + /* This array holds the distance to the nearest-so-far color for each cell */ + JLONG 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 = colorlist[i]; + /* Compute (square of) distance from minc0/c1/c2 to this color */ + inc0 = (minc0 - cinfo->colormap[0][icolor]) * C0_SCALE; + dist0 = inc0 * inc0; + inc1 = (minc1 - cinfo->colormap[1][icolor]) * C1_SCALE; + dist0 += inc1 * inc1; + inc2 = (minc2 - 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)((*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 = (*inptr++) >> C0_SHIFT; + c1 = (*inptr++) >> C1_SHIFT; + c2 = (*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 += inptr[0]; + cur1 += inptr[1]; + cur2 += inptr[2]; + cur0 = range_limit[cur0]; + cur1 = range_limit[cur1]; + cur2 = 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 -= colormap0[pixcode]; + cur1 -= colormap1[pixcode]; + cur2 -= 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; + + bnexterr = cur0; /* Process component 0 */ + errorptr[0] = (FSERROR)(bpreverr0 + cur0 * 3); + bpreverr0 = belowerr0 + cur0 * 5; + belowerr0 = bnexterr; + cur0 *= 7; + bnexterr = cur1; /* Process component 1 */ + errorptr[1] = (FSERROR)(bpreverr1 + cur1 * 3); + bpreverr1 = belowerr1 + cur1 * 5; + belowerr1 = bnexterr; + cur1 *= 7; + bnexterr = cur2; /* Process component 2 */ + errorptr[2] = (FSERROR)(bpreverr2 + cur2 * 3); + bpreverr2 = belowerr2 + cur2 * 5; + belowerr2 = bnexterr; + cur2 *= 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 them 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 *)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 *)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 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 them 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 may affect the memory + * manager's space calculations. 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 */ |