| /* |
| * jquant2.c |
| * |
| * Copyright (C) 1991, 1992, 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 are invoked via the methods color_quant_prescan, |
| * color_quant_doit, and color_quant_init/term. |
| */ |
| |
| #include "jinclude.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. |
| * |
| * The most significant difference between this quantizer and others is that |
| * this one is intended to operate in YCbCr colorspace, rather than RGB space |
| * as is usually done. Actually we work in scaled YCbCr colorspace, where |
| * Y distances are inflated by a factor of 2 relative to Cb or Cr distances. |
| * The empirical evidence is that distances in this space correspond to |
| * perceptual color differences more closely than do distances in RGB space; |
| * and working in this space is inexpensive within a JPEG decompressor, since |
| * the input data is already in YCbCr form. (We could transform to an even |
| * more perceptually linear space such as Lab or Luv, but that is very slow |
| * and doesn't yield much better results than scaled YCbCr.) |
| */ |
| |
| #define Y_SCALE 2 /* scale Y distances up by this much */ |
| |
| #define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */ |
| |
| |
| /* |
| * First we have the histogram data structure and routines for creating it. |
| * |
| * For work in YCbCr space, it is useful to keep more precision for Y than |
| * for Cb or Cr. We recommend keeping 6 bits for Y and 5 bits each for Cb/Cr. |
| * 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 Y value (typically 2^6 = 64 pointers) and |
| * each 2-D array has 2^5^2 = 1024 or 2^6^2 = 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). |
| */ |
| |
| #ifndef HIST_Y_BITS /* so you can override from Makefile */ |
| #define HIST_Y_BITS 6 /* bits of precision in Y histogram */ |
| #endif |
| #ifndef HIST_C_BITS /* so you can override from Makefile */ |
| #define HIST_C_BITS 5 /* bits of precision in Cb/Cr histogram */ |
| #endif |
| |
| #define HIST_Y_ELEMS (1<<HIST_Y_BITS) /* # of elements along histogram axes */ |
| #define HIST_C_ELEMS (1<<HIST_C_BITS) |
| |
| /* These are the amounts to shift an input value to get a histogram index. |
| * For a combination 8/12 bit implementation, would need variables here... |
| */ |
| |
| #define Y_SHIFT (BITS_IN_JSAMPLE-HIST_Y_BITS) |
| #define C_SHIFT (BITS_IN_JSAMPLE-HIST_C_BITS) |
| |
| |
| typedef UINT16 histcell; /* histogram cell; MUST be an unsigned type */ |
| |
| typedef histcell FAR * histptr; /* for pointers to histogram cells */ |
| |
| typedef histcell hist1d[HIST_C_ELEMS]; /* typedefs for the array */ |
| typedef hist1d FAR * hist2d; /* type for the Y-level pointers */ |
| typedef hist2d * hist3d; /* type for top-level pointer */ |
| |
| static hist3d histogram; /* pointer to the histogram */ |
| |
| |
| /* |
| * Prescan some rows of pixels. |
| * In this module the prescan simply updates the histogram, which has been |
| * initialized to zeroes by color_quant_init. |
| * Note: workspace is probably not useful for this routine, but it is passed |
| * anyway to allow some code sharing within the pipeline controller. |
| */ |
| |
| METHODDEF void |
| color_quant_prescan (decompress_info_ptr cinfo, int num_rows, |
| JSAMPIMAGE image_data, JSAMPARRAY workspace) |
| { |
| register JSAMPROW ptr0, ptr1, ptr2; |
| register histptr histp; |
| register int c0, c1, c2; |
| int row; |
| long col; |
| long width = cinfo->image_width; |
| |
| for (row = 0; row < num_rows; row++) { |
| ptr0 = image_data[0][row]; |
| ptr1 = image_data[1][row]; |
| ptr2 = image_data[2][row]; |
| for (col = width; col > 0; col--) { |
| /* get pixel value and index into the histogram */ |
| c0 = GETJSAMPLE(*ptr0++) >> Y_SHIFT; |
| c1 = GETJSAMPLE(*ptr1++) >> C_SHIFT; |
| c2 = GETJSAMPLE(*ptr2++) >> C_SHIFT; |
| histp = & histogram[c0][c1][c2]; |
| /* increment, check for overflow and undo increment if so. */ |
| /* We assume unsigned representation here! */ |
| if (++(*histp) == 0) |
| (*histp)--; |
| } |
| } |
| } |
| |
| |
| /* |
| * Now 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 number of nonzero histogram cells within this box */ |
| long colorcount; |
| } box; |
| typedef box * boxptr; |
| |
| static boxptr boxlist; /* array with room for desired # of boxes */ |
| static int numboxes; /* number of boxes currently in boxlist */ |
| |
| static JSAMPARRAY my_colormap; /* the finished colormap (in YCbCr space) */ |
| |
| |
| LOCAL boxptr |
| find_biggest_color_pop (void) |
| /* Find the splittable box with the largest color population */ |
| /* Returns NULL if no splittable boxes remain */ |
| { |
| register boxptr boxp; |
| register int i; |
| register long max = 0; |
| boxptr which = NULL; |
| |
| for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { |
| if (boxp->colorcount > max) { |
| if (boxp->c0max > boxp->c0min || boxp->c1max > boxp->c1min || |
| boxp->c2max > boxp->c2min) { |
| which = boxp; |
| max = boxp->colorcount; |
| } |
| } |
| } |
| return which; |
| } |
| |
| |
| LOCAL boxptr |
| find_biggest_volume (void) |
| /* Find the splittable box with the largest (scaled) volume */ |
| /* Returns NULL if no splittable boxes remain */ |
| { |
| register boxptr boxp; |
| register int i; |
| register INT32 max = 0; |
| register INT32 norm, c0,c1,c2; |
| boxptr which = NULL; |
| |
| /* We use 2-norm rather than real volume here. |
| * Some care is needed since the differences are expressed in |
| * histogram-cell units; if HIST_Y_BITS != HIST_C_BITS, we have to |
| * adjust the scaling to get the proper scaled-YCbCr-space distance. |
| * This code won't work right if HIST_Y_BITS < HIST_C_BITS, |
| * but that shouldn't ever be true. |
| * Note norm > 0 iff box is splittable, so need not check separately. |
| */ |
| |
| for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { |
| c0 = (boxp->c0max - boxp->c0min) * Y_SCALE; |
| c1 = (boxp->c1max - boxp->c1min) << (HIST_Y_BITS-HIST_C_BITS); |
| c2 = (boxp->c2max - boxp->c2min) << (HIST_Y_BITS-HIST_C_BITS); |
| norm = c0*c0 + c1*c1 + c2*c2; |
| if (norm > max) { |
| which = boxp; |
| max = norm; |
| } |
| } |
| return which; |
| } |
| |
| |
| LOCAL void |
| update_box (boxptr boxp) |
| /* Shrink the min/max bounds of a box to enclose only nonzero elements, */ |
| /* and recompute its population */ |
| { |
| histptr histp; |
| int c0,c1,c2; |
| int c0min,c0max,c1min,c1max,c2min,c2max; |
| 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_C_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_C_ELEMS) |
| if (*histp != 0) { |
| boxp->c2max = c2max = c2; |
| goto have_c2max; |
| } |
| } |
| have_c2max: |
| |
| /* 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 void |
| median_cut (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(); |
| } else { |
| b1 = find_biggest_volume(); |
| } |
| 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 find_biggest_volume about scaling... |
| */ |
| c0 = (b1->c0max - b1->c0min) * Y_SCALE; |
| c1 = (b1->c1max - b1->c1min) << (HIST_Y_BITS-HIST_C_BITS); |
| c2 = (b1->c2max - b1->c2min) << (HIST_Y_BITS-HIST_C_BITS); |
| cmax = c0; n = 0; |
| if (c1 > cmax) { cmax = c1; n = 1; } |
| if (c2 > cmax) { n = 2; } |
| /* 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(b1); |
| update_box(b2); |
| numboxes++; |
| } |
| } |
| |
| |
| LOCAL void |
| compute_color (boxptr boxp, int icolor) |
| /* Compute representative color for a box, put it in my_colormap[icolor] */ |
| { |
| /* Current algorithm: mean weighted by pixels (not colors) */ |
| /* Note it is important to get the rounding correct! */ |
| 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 << Y_SHIFT) + ((1<<Y_SHIFT)>>1)) * count; |
| c1total += ((c1 << C_SHIFT) + ((1<<C_SHIFT)>>1)) * count; |
| c2total += ((c2 << C_SHIFT) + ((1<<C_SHIFT)>>1)) * count; |
| } |
| } |
| } |
| |
| my_colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); |
| my_colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); |
| my_colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); |
| } |
| |
| |
| LOCAL void |
| remap_colormap (decompress_info_ptr cinfo) |
| /* Remap the internal colormap to the output colorspace */ |
| { |
| /* This requires a little trickery since color_convert expects to |
| * deal with 3-D arrays (a 2-D sample array for each component). |
| * We must promote the colormaps into one-row 3-D arrays. |
| */ |
| short ci; |
| JSAMPARRAY input_hack[3]; |
| JSAMPARRAY output_hack[10]; /* assume no more than 10 output components */ |
| |
| for (ci = 0; ci < 3; ci++) |
| input_hack[ci] = &(my_colormap[ci]); |
| for (ci = 0; ci < cinfo->color_out_comps; ci++) |
| output_hack[ci] = &(cinfo->colormap[ci]); |
| |
| (*cinfo->methods->color_convert) (cinfo, 1, |
| (long) cinfo->actual_number_of_colors, |
| input_hack, output_hack); |
| } |
| |
| |
| LOCAL void |
| select_colors (decompress_info_ptr cinfo) |
| /* Master routine for color selection */ |
| { |
| int desired = cinfo->desired_number_of_colors; |
| int i; |
| |
| /* Allocate workspace for box list */ |
| boxlist = (boxptr) (*cinfo->emethods->alloc_small) (desired * SIZEOF(box)); |
| /* Initialize one box containing whole space */ |
| numboxes = 1; |
| boxlist[0].c0min = 0; |
| boxlist[0].c0max = MAXJSAMPLE >> Y_SHIFT; |
| boxlist[0].c1min = 0; |
| boxlist[0].c1max = MAXJSAMPLE >> C_SHIFT; |
| boxlist[0].c2min = 0; |
| boxlist[0].c2max = MAXJSAMPLE >> C_SHIFT; |
| /* Shrink it to actually-used volume and set its statistics */ |
| update_box(& boxlist[0]); |
| /* Perform median-cut to produce final box list */ |
| median_cut(desired); |
| /* Compute the representative color for each box, fill my_colormap[] */ |
| for (i = 0; i < numboxes; i++) |
| compute_color(& boxlist[i], i); |
| cinfo->actual_number_of_colors = numboxes; |
| /* Produce an output colormap in the desired output colorspace */ |
| remap_colormap(cinfo); |
| TRACEMS1(cinfo->emethods, 1, "Selected %d colors for quantization", |
| numboxes); |
| /* Done with the box list */ |
| (*cinfo->emethods->free_small) ((void *) boxlist); |
| } |
| |
| |
| /* |
| * 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. |
| * Another disadvantage of the brute force approach is that it computes |
| * distances to every cell of the cubical histogram. When working with YCbCr |
| * input, only about a quarter of the cube represents realizable colors, so |
| * many of the cells will never be used and filling them is wasted effort. |
| * |
| * 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. A subbox is processed only when some cell in it is |
| * referenced by the pass2 routines, so we will never bother with cells far |
| * outside the realizable color volume. 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. |
| */ |
| |
| |
| #ifndef BOX_Y_LOG /* so you can override from Makefile */ |
| #define BOX_Y_LOG (HIST_Y_BITS-3) /* log2(hist cells in update box, Y axis) */ |
| #endif |
| #ifndef BOX_C_LOG /* so you can override from Makefile */ |
| #define BOX_C_LOG (HIST_C_BITS-3) /* log2(hist cells in update box, C axes) */ |
| #endif |
| |
| #define BOX_Y_ELEMS (1<<BOX_Y_LOG) /* # of hist cells in update box */ |
| #define BOX_C_ELEMS (1<<BOX_C_LOG) |
| |
| #define BOX_Y_SHIFT (Y_SHIFT + BOX_Y_LOG) |
| #define BOX_C_SHIFT (C_SHIFT + BOX_C_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 (decompress_info_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_Y_SHIFT) - (1 << Y_SHIFT)); |
| centerc0 = (minc0 + maxc0) >> 1; |
| maxc1 = minc1 + ((1 << BOX_C_SHIFT) - (1 << C_SHIFT)); |
| centerc1 = (minc1 + maxc1) >> 1; |
| maxc2 = minc2 + ((1 << BOX_C_SHIFT) - (1 << C_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. |
| * Note we have to scale Y to get correct distance in scaled space. |
| */ |
| minmaxdist = 0x7FFFFFFFL; |
| |
| for (i = 0; i < numcolors; i++) { |
| /* We compute the squared-c0-distance term, then add in the other two. */ |
| x = GETJSAMPLE(my_colormap[0][i]); |
| if (x < minc0) { |
| tdist = (x - minc0) * Y_SCALE; |
| min_dist = tdist*tdist; |
| tdist = (x - maxc0) * Y_SCALE; |
| max_dist = tdist*tdist; |
| } else if (x > maxc0) { |
| tdist = (x - maxc0) * Y_SCALE; |
| min_dist = tdist*tdist; |
| tdist = (x - minc0) * Y_SCALE; |
| max_dist = tdist*tdist; |
| } else { |
| /* within cell range so no contribution to min_dist */ |
| min_dist = 0; |
| if (x <= centerc0) { |
| tdist = (x - maxc0) * Y_SCALE; |
| max_dist = tdist*tdist; |
| } else { |
| tdist = (x - minc0) * Y_SCALE; |
| max_dist = tdist*tdist; |
| } |
| } |
| |
| x = GETJSAMPLE(my_colormap[1][i]); |
| if (x < minc1) { |
| tdist = x - minc1; |
| min_dist += tdist*tdist; |
| tdist = x - maxc1; |
| max_dist += tdist*tdist; |
| } else if (x > maxc1) { |
| tdist = x - maxc1; |
| min_dist += tdist*tdist; |
| tdist = x - minc1; |
| max_dist += tdist*tdist; |
| } else { |
| /* within cell range so no contribution to min_dist */ |
| if (x <= centerc1) { |
| tdist = x - maxc1; |
| max_dist += tdist*tdist; |
| } else { |
| tdist = x - minc1; |
| max_dist += tdist*tdist; |
| } |
| } |
| |
| x = GETJSAMPLE(my_colormap[2][i]); |
| if (x < minc2) { |
| tdist = x - minc2; |
| min_dist += tdist*tdist; |
| tdist = x - maxc2; |
| max_dist += tdist*tdist; |
| } else if (x > maxc2) { |
| tdist = x - maxc2; |
| min_dist += tdist*tdist; |
| tdist = x - minc2; |
| max_dist += tdist*tdist; |
| } else { |
| /* within cell range so no contribution to min_dist */ |
| if (x <= centerc2) { |
| tdist = x - maxc2; |
| max_dist += tdist*tdist; |
| } else { |
| tdist = x - minc2; |
| 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 (decompress_info_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_Y_ELEMS * BOX_C_ELEMS * BOX_C_ELEMS]; |
| |
| /* Initialize best-distance for each cell of the update box */ |
| bptr = bestdist; |
| for (i = BOX_Y_ELEMS*BOX_C_ELEMS*BOX_C_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. |
| * Note we have to scale Y to get correct distance in scaled space. |
| */ |
| |
| /* Nominal steps between cell centers ("x" in Thomas article) */ |
| #define STEP_Y ((1 << Y_SHIFT) * Y_SCALE) |
| #define STEP_C (1 << C_SHIFT) |
| |
| for (i = 0; i < numcolors; i++) { |
| icolor = GETJSAMPLE(colorlist[i]); |
| /* Compute (square of) distance from minc0/c1/c2 to this color */ |
| inc0 = (minc0 - (int) GETJSAMPLE(my_colormap[0][icolor])) * Y_SCALE; |
| dist0 = inc0*inc0; |
| inc1 = minc1 - (int) GETJSAMPLE(my_colormap[1][icolor]); |
| dist0 += inc1*inc1; |
| inc2 = minc2 - (int) GETJSAMPLE(my_colormap[2][icolor]); |
| dist0 += inc2*inc2; |
| /* Form the initial difference increments */ |
| inc0 = inc0 * (2 * STEP_Y) + STEP_Y * STEP_Y; |
| inc1 = inc1 * (2 * STEP_C) + STEP_C * STEP_C; |
| inc2 = inc2 * (2 * STEP_C) + STEP_C * STEP_C; |
| /* Now loop over all cells in box, updating distance per Thomas method */ |
| bptr = bestdist; |
| cptr = bestcolor; |
| xx0 = inc0; |
| for (ic0 = BOX_Y_ELEMS-1; ic0 >= 0; ic0--) { |
| dist1 = dist0; |
| xx1 = inc1; |
| for (ic1 = BOX_C_ELEMS-1; ic1 >= 0; ic1--) { |
| dist2 = dist1; |
| xx2 = inc2; |
| for (ic2 = BOX_C_ELEMS-1; ic2 >= 0; ic2--) { |
| if (dist2 < *bptr) { |
| *bptr = dist2; |
| *cptr = (JSAMPLE) icolor; |
| } |
| dist2 += xx2; |
| xx2 += 2 * STEP_C * STEP_C; |
| bptr++; |
| cptr++; |
| } |
| dist1 += xx1; |
| xx1 += 2 * STEP_C * STEP_C; |
| } |
| dist0 += xx0; |
| xx0 += 2 * STEP_Y * STEP_Y; |
| } |
| } |
| } |
| |
| |
| LOCAL void |
| fill_inverse_cmap (decompress_info_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.) */ |
| { |
| 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_Y_ELEMS * BOX_C_ELEMS * BOX_C_ELEMS]; |
| |
| /* Convert cell coordinates to update box ID */ |
| c0 >>= BOX_Y_LOG; |
| c1 >>= BOX_C_LOG; |
| c2 >>= BOX_C_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_Y_SHIFT) + ((1 << Y_SHIFT) >> 1); |
| minc1 = (c1 << BOX_C_SHIFT) + ((1 << C_SHIFT) >> 1); |
| minc2 = (c2 << BOX_C_SHIFT) + ((1 << C_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_Y_LOG; /* convert ID back to base cell indexes */ |
| c1 <<= BOX_C_LOG; |
| c2 <<= BOX_C_LOG; |
| cptr = bestcolor; |
| for (ic0 = 0; ic0 < BOX_Y_ELEMS; ic0++) { |
| for (ic1 = 0; ic1 < BOX_C_ELEMS; ic1++) { |
| cachep = & histogram[c0+ic0][c1+ic1][c2]; |
| for (ic2 = 0; ic2 < BOX_C_ELEMS; ic2++) { |
| *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); |
| } |
| } |
| } |
| } |
| |
| |
| /* |
| * These routines perform second-pass scanning of the image: map each pixel to |
| * the proper colormap index, and output the indexes to the output file. |
| * |
| * output_workspace is a one-component array of pixel dimensions at least |
| * as large as the input image strip; it can be used to hold the converted |
| * pixels' colormap indexes. |
| */ |
| |
| METHODDEF void |
| pass2_nodither (decompress_info_ptr cinfo, int num_rows, |
| JSAMPIMAGE image_data, JSAMPARRAY output_workspace) |
| /* This version performs no dithering */ |
| { |
| register JSAMPROW ptr0, ptr1, ptr2, outptr; |
| register histptr cachep; |
| register int c0, c1, c2; |
| int row; |
| long col; |
| long width = cinfo->image_width; |
| |
| /* Convert data to colormap indexes, which we save in output_workspace */ |
| for (row = 0; row < num_rows; row++) { |
| ptr0 = image_data[0][row]; |
| ptr1 = image_data[1][row]; |
| ptr2 = image_data[2][row]; |
| outptr = output_workspace[row]; |
| for (col = width; col > 0; col--) { |
| /* get pixel value and index into the cache */ |
| c0 = GETJSAMPLE(*ptr0++) >> Y_SHIFT; |
| c1 = GETJSAMPLE(*ptr1++) >> C_SHIFT; |
| c2 = GETJSAMPLE(*ptr2++) >> C_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); |
| } |
| } |
| /* Emit converted rows to the output file */ |
| (*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &output_workspace); |
| } |
| |
| |
| /* Declarations for Floyd-Steinberg dithering. |
| * |
| * Errors are accumulated into the arrays evenrowerrs[] and oddrowerrs[]. |
| * These have resolutions of 1/16th of a pixel count. The error at a given |
| * pixel is propagated to its unprocessed 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. |
| * |
| * Each of the arrays 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. |
| * In evenrowerrs[], the entries for a component are stored left-to-right, but |
| * in oddrowerrs[] they are stored right-to-left. This means we always |
| * process the current row's error entries in increasing order and the next |
| * row's error entries in decreasing order, regardless of whether we are |
| * working L-to-R or R-to-L in the pixel data! |
| * |
| * Note: on a wide image, we might not have enough room in a PC's near data |
| * segment to hold the error arrays; so they are allocated with alloc_medium. |
| */ |
| |
| #ifdef EIGHT_BIT_SAMPLES |
| typedef INT16 FSERROR; /* 16 bits should be enough */ |
| #else |
| typedef INT32 FSERROR; /* may need more than 16 bits? */ |
| #endif |
| |
| typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */ |
| |
| static FSERRPTR evenrowerrs, oddrowerrs; /* current-row and next-row errors */ |
| static boolean on_odd_row; /* flag to remember which row we are on */ |
| |
| |
| METHODDEF void |
| pass2_dither (decompress_info_ptr cinfo, int num_rows, |
| JSAMPIMAGE image_data, JSAMPARRAY output_workspace) |
| /* This version performs Floyd-Steinberg dithering */ |
| { |
| #ifdef EIGHT_BIT_SAMPLES |
| register int c0, c1, c2; |
| int two_val; |
| #else |
| register FSERROR c0, c1, c2; |
| FSERROR two_val; |
| #endif |
| register FSERRPTR thisrowerr, nextrowerr; |
| JSAMPROW ptr0, ptr1, ptr2, outptr; |
| histptr cachep; |
| register int pixcode; |
| int dir; |
| int row; |
| long col; |
| long width = cinfo->image_width; |
| JSAMPLE *range_limit = cinfo->sample_range_limit; |
| JSAMPROW colormap0 = my_colormap[0]; |
| JSAMPROW colormap1 = my_colormap[1]; |
| JSAMPROW colormap2 = my_colormap[2]; |
| SHIFT_TEMPS |
| |
| /* Convert data to colormap indexes, which we save in output_workspace */ |
| for (row = 0; row < num_rows; row++) { |
| ptr0 = image_data[0][row]; |
| ptr1 = image_data[1][row]; |
| ptr2 = image_data[2][row]; |
| outptr = output_workspace[row]; |
| if (on_odd_row) { |
| /* work right to left in this row */ |
| ptr0 += width - 1; |
| ptr1 += width - 1; |
| ptr2 += width - 1; |
| outptr += width - 1; |
| dir = -1; |
| thisrowerr = oddrowerrs + 3; |
| nextrowerr = evenrowerrs + width*3; |
| on_odd_row = FALSE; /* flip for next time */ |
| } else { |
| /* work left to right in this row */ |
| dir = 1; |
| thisrowerr = evenrowerrs + 3; |
| nextrowerr = oddrowerrs + width*3; |
| on_odd_row = TRUE; /* flip for next time */ |
| } |
| /* need only initialize this one entry in nextrowerr */ |
| nextrowerr[0] = nextrowerr[1] = nextrowerr[2] = 0; |
| for (col = width; col > 0; col--) { |
| /* For each component, get accumulated error and round to integer; |
| * form pixel value + error, and range-limit to 0..MAXJSAMPLE. |
| * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct |
| * for either sign of the error value. Max error is +- MAXJSAMPLE. |
| */ |
| c0 = RIGHT_SHIFT(thisrowerr[0] + 8, 4); |
| c1 = RIGHT_SHIFT(thisrowerr[1] + 8, 4); |
| c2 = RIGHT_SHIFT(thisrowerr[2] + 8, 4); |
| c0 += GETJSAMPLE(*ptr0); |
| c1 += GETJSAMPLE(*ptr1); |
| c2 += GETJSAMPLE(*ptr2); |
| c0 = GETJSAMPLE(range_limit[c0]); |
| c1 = GETJSAMPLE(range_limit[c1]); |
| c2 = GETJSAMPLE(range_limit[c2]); |
| /* Index into the cache with adjusted pixel value */ |
| cachep = & histogram[c0 >> Y_SHIFT][c1 >> C_SHIFT][c2 >> C_SHIFT]; |
| /* If we have not seen this color before, find nearest colormap */ |
| /* entry and update the cache */ |
| if (*cachep == 0) |
| fill_inverse_cmap(cinfo, c0 >> Y_SHIFT, c1 >> C_SHIFT, c2 >> C_SHIFT); |
| /* Now emit the colormap index for this cell */ |
| pixcode = *cachep - 1; |
| *outptr = (JSAMPLE) pixcode; |
| /* Compute representation error for this pixel */ |
| c0 -= GETJSAMPLE(colormap0[pixcode]); |
| c1 -= GETJSAMPLE(colormap1[pixcode]); |
| c2 -= GETJSAMPLE(colormap2[pixcode]); |
| /* Propagate error to adjacent pixels */ |
| /* Remember that nextrowerr entries are in reverse order! */ |
| two_val = c0 * 2; |
| nextrowerr[0-3] = c0; /* not +=, since not initialized yet */ |
| c0 += two_val; /* form error * 3 */ |
| nextrowerr[0+3] += c0; |
| c0 += two_val; /* form error * 5 */ |
| nextrowerr[0 ] += c0; |
| c0 += two_val; /* form error * 7 */ |
| thisrowerr[0+3] += c0; |
| two_val = c1 * 2; |
| nextrowerr[1-3] = c1; /* not +=, since not initialized yet */ |
| c1 += two_val; /* form error * 3 */ |
| nextrowerr[1+3] += c1; |
| c1 += two_val; /* form error * 5 */ |
| nextrowerr[1 ] += c1; |
| c1 += two_val; /* form error * 7 */ |
| thisrowerr[1+3] += c1; |
| two_val = c2 * 2; |
| nextrowerr[2-3] = c2; /* not +=, since not initialized yet */ |
| c2 += two_val; /* form error * 3 */ |
| nextrowerr[2+3] += c2; |
| c2 += two_val; /* form error * 5 */ |
| nextrowerr[2 ] += c2; |
| c2 += two_val; /* form error * 7 */ |
| thisrowerr[2+3] += c2; |
| /* Advance to next column */ |
| ptr0 += dir; |
| ptr1 += dir; |
| ptr2 += dir; |
| outptr += dir; |
| thisrowerr += 3; /* cur-row error ptr advances to right */ |
| nextrowerr -= 3; /* next-row error ptr advances to left */ |
| } |
| } |
| /* Emit converted rows to the output file */ |
| (*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &output_workspace); |
| } |
| |
| |
| /* |
| * Initialize for two-pass color quantization. |
| */ |
| |
| METHODDEF void |
| color_quant_init (decompress_info_ptr cinfo) |
| { |
| int i; |
| |
| /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */ |
| if (cinfo->desired_number_of_colors < 8) |
| ERREXIT(cinfo->emethods, "Cannot request less than 8 quantized colors"); |
| /* Make sure colormap indexes can be represented by JSAMPLEs */ |
| if (cinfo->desired_number_of_colors > MAXNUMCOLORS) |
| ERREXIT1(cinfo->emethods, "Cannot request more than %d quantized colors", |
| MAXNUMCOLORS); |
| |
| /* Allocate and zero the histogram */ |
| histogram = (hist3d) (*cinfo->emethods->alloc_small) |
| (HIST_Y_ELEMS * SIZEOF(hist2d)); |
| for (i = 0; i < HIST_Y_ELEMS; i++) { |
| histogram[i] = (hist2d) (*cinfo->emethods->alloc_medium) |
| (HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell)); |
| jzero_far((void FAR *) histogram[i], |
| HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell)); |
| } |
| |
| /* Allocate storage for the internal and external colormaps. */ |
| /* We do this now since it is FAR storage and may affect the memory */ |
| /* manager's space calculations. */ |
| my_colormap = (*cinfo->emethods->alloc_small_sarray) |
| ((long) cinfo->desired_number_of_colors, |
| (long) 3); |
| cinfo->colormap = (*cinfo->emethods->alloc_small_sarray) |
| ((long) cinfo->desired_number_of_colors, |
| (long) cinfo->color_out_comps); |
| |
| /* Allocate Floyd-Steinberg workspace if necessary */ |
| /* This isn't needed until pass 2, but again it is FAR storage. */ |
| if (cinfo->use_dithering) { |
| size_t arraysize = (size_t) ((cinfo->image_width + 2L) * 3L * SIZEOF(FSERROR)); |
| |
| evenrowerrs = (FSERRPTR) (*cinfo->emethods->alloc_medium) (arraysize); |
| oddrowerrs = (FSERRPTR) (*cinfo->emethods->alloc_medium) (arraysize); |
| /* we only need to zero the forward contribution for current row. */ |
| jzero_far((void FAR *) evenrowerrs, arraysize); |
| on_odd_row = FALSE; |
| } |
| |
| /* Indicate number of passes needed, excluding the prescan pass. */ |
| cinfo->total_passes++; /* I always use one pass */ |
| } |
| |
| |
| /* |
| * Perform two-pass quantization: rescan the image data and output the |
| * converted data via put_color_map and put_pixel_rows. |
| * The source_method is a routine that can scan the image data; it can |
| * be called as many times as desired. The processing routine called by |
| * source_method has the same interface as color_quantize does in the |
| * one-pass case, except it must call put_pixel_rows itself. (This allows |
| * me to use multiple passes in which earlier passes don't output anything.) |
| */ |
| |
| METHODDEF void |
| color_quant_doit (decompress_info_ptr cinfo, quantize_caller_ptr source_method) |
| { |
| int i; |
| |
| /* Select the representative colors */ |
| select_colors(cinfo); |
| /* Pass the external colormap to the output module. */ |
| /* NB: the output module may continue to use the colormap until shutdown. */ |
| (*cinfo->methods->put_color_map) (cinfo, cinfo->actual_number_of_colors, |
| cinfo->colormap); |
| /* Re-zero the histogram so pass 2 can use it as nearest-color cache */ |
| for (i = 0; i < HIST_Y_ELEMS; i++) { |
| jzero_far((void FAR *) histogram[i], |
| HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell)); |
| } |
| /* Perform pass 2 */ |
| if (cinfo->use_dithering) |
| (*source_method) (cinfo, pass2_dither); |
| else |
| (*source_method) (cinfo, pass2_nodither); |
| } |
| |
| |
| /* |
| * Finish up at the end of the file. |
| */ |
| |
| METHODDEF void |
| color_quant_term (decompress_info_ptr cinfo) |
| { |
| /* no work (we let free_all release the histogram/cache and colormaps) */ |
| /* Note that we *mustn't* free the external colormap before free_all, */ |
| /* since output module may use it! */ |
| } |
| |
| |
| /* |
| * Map some rows of pixels to the output colormapped representation. |
| * Not used in two-pass case. |
| */ |
| |
| METHODDEF void |
| color_quantize (decompress_info_ptr cinfo, int num_rows, |
| JSAMPIMAGE input_data, JSAMPARRAY output_data) |
| { |
| ERREXIT(cinfo->emethods, "Should not get here!"); |
| } |
| |
| |
| /* |
| * The method selection routine for 2-pass color quantization. |
| */ |
| |
| GLOBAL void |
| jsel2quantize (decompress_info_ptr cinfo) |
| { |
| if (cinfo->two_pass_quantize) { |
| /* Make sure jdmaster didn't give me a case I can't handle */ |
| if (cinfo->num_components != 3 || cinfo->jpeg_color_space != CS_YCbCr) |
| ERREXIT(cinfo->emethods, "2-pass quantization only handles YCbCr input"); |
| cinfo->methods->color_quant_init = color_quant_init; |
| cinfo->methods->color_quant_prescan = color_quant_prescan; |
| cinfo->methods->color_quant_doit = color_quant_doit; |
| cinfo->methods->color_quant_term = color_quant_term; |
| cinfo->methods->color_quantize = color_quantize; |
| } |
| } |
| |
| #endif /* QUANT_2PASS_SUPPORTED */ |