/* * Copyright 2005 Ben Hutchings. * * This is derived from jquant2.c and parts of jdmaster.c, jmorecfg.h, * jpegint.h and jpeglib.h, from the Independent JPEG Group's software. * Copyright (C) 1991-1998, Thomas G. Lane. * * 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. */ /* * These definitions cover what would normally be defind in jconfig.h, * jmorecfg.h, jpegint.h and jpeglib.h. */ #include <stdlib.h> #include <stdint.h> #include <string.h> #include "jquant2.h" /* * This controls whether an additional alpha channel is expected in the * input buffer. If so, output color 0 is reserved for fully transparent * pixels and the alpha channel is otherwise ignored. That is, only * binary transparency is really supported. */ #define HAS_ALPHA 1 typedef uint16_t UINT16; typedef int16_t INT16; typedef int32_t INT32; typedef int boolean; #define BITS_IN_JSAMPLE 8 #define MAXJSAMPLE 255 #define CENTERJSAMPLE 128 #define GETJSAMPLE(value) ((int) (value)) typedef int JDIMENSION; #define RGB_RED 0 #define RGB_GREEN 1 #define RGB_BLUE 2 #define FAR #define SIZEOF sizeof #define TRUE 1 #define FALSE 0 #ifndef NULL #define NULL 0 #endif #define JMETHOD(return_type, name, param_list) return_type (* name) param_list #define METHODDEF(return_type) static return_type #define LOCAL(return_type) static return_type #define GLOBAL(return_type) return_type #define TRACEMS1(cinfo, b, message_num, param_1) /* FIXME */ #define ERREXIT(cinfo, message_num) abort() #define ERREXIT1(cinfo, message_num, param_1) abort() #define SHIFT_TEMPS #define RIGHT_SHIFT(x,shft) ((x) >> (shft)) #define jzero_far(base, length) memset(base, 0, length) #define MEMZERO(base, length) memset(base, 0, length) #define MEMCOPY(dest, source, length) memcpy(dest, source, length) 00082 struct jpeg_decompress_struct { J_DITHER_MODE dither_mode; /* type of color dithering to use */ /* Description of actual output image that will be returned to application. * These fields are computed by jpeg_start_decompress(). * You can also use jpeg_calc_output_dimensions() to determine these values * in advance of calling jpeg_start_decompress(). */ JDIMENSION output_width; /* scaled image width */ JDIMENSION output_height; /* scaled image height */ /* When quantizing colors, the output colormap is described by these fields. * The application can supply a colormap by setting colormap non-NULL before * calling jpeg_start_decompress; otherwise a colormap is created during * jpeg_start_decompress or jpeg_start_output. * The map has 3 rows and actual_number_of_colors columns. */ int actual_number_of_colors; /* number of entries in use */ JSAMPARRAY colormap; /* The color map as a 2-D pixel array */ JSAMPLE * sample_range_limit; /* table for fast range-limiting */ struct jpeg_color_quantizer * cquantize; }; typedef struct jpeg_decompress_struct * j_decompress_ptr; typedef j_decompress_ptr j_common_ptr; 00110 struct jpeg_color_quantizer { JMETHOD(void, start_pass, (j_decompress_ptr cinfo, boolean is_pre_scan)); JMETHOD(void, color_quantize, (j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)); JMETHOD(void, finish_pass, (j_decompress_ptr cinfo)); }; /* * 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 */ 00284 typedef struct { struct jpeg_color_quantizer pub; /* public fields */ /* Space for the eventually created colormap is stashed here */ JSAMPROW sv_colormap[3]; /* 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--) { /* ignore transparent pixels */ if (!HAS_ALPHA || GETJSAMPLE(ptr[3]) != 0) { /* 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+HAS_ALPHA; } } } /* * 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). */ 00352 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; } } } if (total) { 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); } else { cinfo->colormap[0][icolor] = 0; cinfo->colormap[1][icolor] = 0; cinfo->colormap[2][icolor] = 0; } } 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) malloc(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; if (HAS_ALPHA && *inptr++ == 0) { *outptr++ = 0; } else { 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 + HAS_ALPHA); } } } } 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 dir_comp; /* 3*dir or 4*dir, for advancing inptr */ int dir3; /* 3*dir, for advancing 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+HAS_ALPHA); /* so point to rightmost pixel */ outptr += width-1; dir = -1; dir3 = -3; dir_comp = -3-HAS_ALPHA; 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; dir_comp = 3+HAS_ALPHA; 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--) { if (HAS_ALPHA && inptr[3] == 0) { /* Output transparent pixel and reset error values. */ *outptr = 0; cur0 = cur1 = cur2 = 0; } else { /* 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[dir_comp+0] + 8, 4); cur1 = RIGHT_SHIFT(cur1 + errorptr[dir_comp+1] + 8, 4); cur2 = RIGHT_SHIFT(cur2 + errorptr[dir_comp+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 + HAS_ALPHA; /* 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 += dir_comp; /* 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 *) malloc((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; 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) malloc(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; } } /* * Several decompression processes need to range-limit values to the range * 0..MAXJSAMPLE; the input value may fall somewhat outside this range * due to noise introduced by quantization, roundoff error, etc. These * processes are inner loops and need to be as fast as possible. On most * machines, particularly CPUs with pipelines or instruction prefetch, * a (subscript-check-less) C table lookup * x = sample_range_limit[x]; * is faster than explicit tests * if (x < 0) x = 0; * else if (x > MAXJSAMPLE) x = MAXJSAMPLE; * These processes all use a common table prepared by the routine below. * * For most steps we can mathematically guarantee that the initial value * of x is within MAXJSAMPLE+1 of the legal range, so a table running from * -(MAXJSAMPLE+1) to 2*MAXJSAMPLE+1 is sufficient. But for the initial * limiting step (just after the IDCT), a wildly out-of-range value is * possible if the input data is corrupt. To avoid any chance of indexing * off the end of memory and getting a bad-pointer trap, we perform the * post-IDCT limiting thus: * x = range_limit[x & MASK]; * where MASK is 2 bits wider than legal sample data, ie 10 bits for 8-bit * samples. Under normal circumstances this is more than enough range and * a correct output will be generated; with bogus input data the mask will * cause wraparound, and we will safely generate a bogus-but-in-range output. * For the post-IDCT step, we want to convert the data from signed to unsigned * representation by adding CENTERJSAMPLE at the same time that we limit it. * So the post-IDCT limiting table ends up looking like this: * CENTERJSAMPLE,CENTERJSAMPLE+1,...,MAXJSAMPLE, * MAXJSAMPLE (repeat 2*(MAXJSAMPLE+1)-CENTERJSAMPLE times), * 0 (repeat 2*(MAXJSAMPLE+1)-CENTERJSAMPLE times), * 0,1,...,CENTERJSAMPLE-1 * Negative inputs select values from the upper half of the table after * masking. * * We can save some space by overlapping the start of the post-IDCT table * with the simpler range limiting table. The post-IDCT table begins at * sample_range_limit + CENTERJSAMPLE. * * Note that the table is allocated in near data space on PCs; it's small * enough and used often enough to justify this. */ LOCAL(void) prepare_range_limit_table (j_decompress_ptr cinfo) /* Allocate and fill in the sample_range_limit table */ { JSAMPLE * table; int i; table = (JSAMPLE *) malloc( (5 * (MAXJSAMPLE+1) + CENTERJSAMPLE) * SIZEOF(JSAMPLE)); table += (MAXJSAMPLE+1); /* allow negative subscripts of simple table */ cinfo->sample_range_limit = table; /* First segment of "simple" table: limit[x] = 0 for x < 0 */ MEMZERO(table - (MAXJSAMPLE+1), (MAXJSAMPLE+1) * SIZEOF(JSAMPLE)); /* Main part of "simple" table: limit[x] = x */ for (i = 0; i <= MAXJSAMPLE; i++) table[i] = (JSAMPLE) i; table += CENTERJSAMPLE; /* Point to where post-IDCT table starts */ /* End of simple table, rest of first half of post-IDCT table */ for (i = CENTERJSAMPLE; i < 2*(MAXJSAMPLE+1); i++) table[i] = MAXJSAMPLE; /* Second half of post-IDCT table */ MEMZERO(table + (2 * (MAXJSAMPLE+1)), (2 * (MAXJSAMPLE+1) - CENTERJSAMPLE) * SIZEOF(JSAMPLE)); MEMCOPY(table + (4 * (MAXJSAMPLE+1) - CENTERJSAMPLE), cinfo->sample_range_limit, CENTERJSAMPLE * SIZEOF(JSAMPLE)); } void quantize (JSAMPARRAY input_buf, JSAMPARRAY output_buf, int width, int height, J_DITHER_MODE dither_mode, int desired /*number_of_colors*/, unsigned int * output_colors) { struct jpeg_decompress_struct cinfo_buf = {}, * cinfo; my_cquantizer cquantize_buf = {}, * cquantize; int i; int pass_flag; cinfo = &cinfo_buf; cinfo->dither_mode = dither_mode; cinfo->output_width = width; cinfo->output_height = height; prepare_range_limit_table(cinfo); cquantize = &cquantize_buf; cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; cquantize->pub.start_pass = start_pass_2_quant; cquantize->fserrors = NULL; /* flag optional arrays not allocated */ cquantize->error_limiter = NULL; /* Allocate the histogram/inverse colormap storage */ cquantize->histogram = (hist3d) malloc(HIST_C0_ELEMS * SIZEOF(hist2d)); for (i = 0; i < HIST_C0_ELEMS; i++) { cquantize->histogram[i] = (hist2d) malloc( HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); } /* Allocate storage for the completed colormap, if required. */ if (desired < 1+HAS_ALPHA) ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1+HAS_ALPHA); /* Make sure colormap indexes can be represented by JSAMPLEs */ if (desired > MAXNUMCOLORS) ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); desired -= HAS_ALPHA; cquantize->sv_colormap[0] = malloc((JDIMENSION) desired * (JDIMENSION) 3); cquantize->sv_colormap[1] = cquantize->sv_colormap[0] + (JDIMENSION) desired; cquantize->sv_colormap[2] = cquantize->sv_colormap[1] + (JDIMENSION) desired; cquantize->desired = desired; for (pass_flag = 1; pass_flag >= 0; --pass_flag) { start_pass_2_quant(cinfo, pass_flag); cquantize->pub.color_quantize(cinfo, input_buf, output_buf, height); cquantize->pub.finish_pass(cinfo); } if (HAS_ALPHA) output_colors[0] = 0; for (i = 0; i != desired; ++i) { output_colors[HAS_ALPHA+i] = (0xFF000000 + (cquantize->sv_colormap[2][i] << 16) + (cquantize->sv_colormap[1][i] << 8) + cquantize->sv_colormap[0][i]); } if (cinfo->sample_range_limit) free(cinfo->sample_range_limit - (MAXJSAMPLE+1)); free(cquantize->sv_colormap[0]); if (cquantize->histogram) { for (i = 0; i < HIST_C0_ELEMS; i++) free(cquantize->histogram[i]); free(cquantize->histogram); } free(cquantize->fserrors); if (cquantize->error_limiter) free(cquantize->error_limiter - MAXJSAMPLE); }

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