// * jquant2.c // * // * Copyright (C) 1991-1995, Thomas G. Lane. // * This file is part of the Independent JPEG Group's software. // * For conditions of distribution and use, see the accompanying README file. // * // * This file contains 2-pass color quantization (color mapping) routines. // * These routines provide selection of a custom color map for an image, // * followed by mapping of the image to that color map, with optional // * Floyd-Steinberg dithering. // * It is also possible to use just the second pass to map to an arbitrary // * externally-given color map. // * // * Note: ordered dithering is not supported, since there isn't any fast // * way to compute intercolor distances; it's unclear that ordered dither's // * fundamental assumptions even hold with an irregularly spaced color map. #define JPEG_INTERNALS #include "JINCLUDE.h" #include "JPEGLIB.h" #ifdef QUANT_2PASS_SUPPORTED // * This module implements the well-known Heckbert paradigm for color // * quantization. Most of the ideas used here can be traced back to // * Heckbert's seminal paper // * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display", // * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304. // * // * In the first pass over the image, we accumulate a histogram showing the // * usage count of each possible color. To keep the histogram to a reasonable // * size, we reduce the precision of the input; typical practice is to retain // * 5 or 6 bits per color, so that 8 or 4 different input values are counted // * in the same histogram cell. // * // * Next, the color-selection step begins with a box representing the whole // * color space, and repeatedly splits the "largest" remaining box until we // * have as many boxes as desired colors. Then the mean color in each // * remaining box becomes one of the possible output colors. // * // * The second pass over the image maps each input pixel to the closest output // * color (optionally after applying a Floyd-Steinberg dithering correction). // * This mapping is logically trivial, but making it go fast enough requires // * considerable care. // * // * Heckbert-style quantizers vary a good deal in their policies for choosing // * the "largest" box and deciding where to cut it. The particular policies // * used here have proved out well in experimental comparisons, but better ones // * may yet be found. // * // * In earlier versions of the IJG code, this module quantized in YCbCr color // * space, processing the raw upsampled data without a color conversion step. // * This allowed the color conversion math to be done only once per colormap // * entry, not once per pixel. However, that optimization precluded other // * useful optimizations (such as merging color conversion with upsampling) // * and it also interfered with desired capabilities such as quantizing to an // * externally-supplied colormap. We have therefore abandoned that approach. // * The present code works in the post-conversion color space, typically RGB. // * // * To improve the visual quality of the results, we actually work in scaled // * RGB space, giving G distances more weight than R, and R in turn more than // * B. To do everything in integer math, we must use integer scale factors. // * The 2/3/1 scale factors used here correspond loosely to the relative // * weights of the colors in the NTSC grayscale equation. // * If you want to use this code to quantize a non-RGB color space, you'll // * probably need to change these scale factors. #define R_SCALE 2 // scale R distances by this much #define G_SCALE 3 // scale G distances by this much #define B_SCALE 1 // and B by this much // Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined // * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B // * and B,G,R orders. If you define some other weird order in jmorecfg.h, // * you'll get compile errors until you extend this logic. In that case // * you'll probably want to tweak the histogram sizes too. #if RGB_RED == 0 #define C0_SCALE R_SCALE #endif #if RGB_BLUE == 0 #define C0_SCALE B_SCALE #endif #if RGB_GREEN == 1 #define C1_SCALE G_SCALE #endif #if RGB_RED == 2 #define C2_SCALE R_SCALE #endif #if RGB_BLUE == 2 #define C2_SCALE B_SCALE #endif // * First we have the histogram data structure and routines for creating it. // * // * The number of bits of precision can be adjusted by changing these symbols. // * We recommend keeping 6 bits for G and 5 each for R and B. // * If you have plenty of memory and cycles, 6 bits all around gives marginally // * better results; if you are short of memory, 5 bits all around will save // * some space but degrade the results. // * To maintain a fully accurate histogram, we'd need to allocate a "long" // * (preferably unsigned long) for each cell. In practice this is overkill; // * we can get by with 16 bits per cell. Few of the cell counts will overflow, // * and clamping those that do overflow to the maximum value will give close- // * enough results. This reduces the recommended histogram size from 256Kb // * to 128Kb, which is a useful savings on PC-class machines. // * (In the second pass the histogram space is re-used for pixel mapping data; // * in that capacity, each cell must be able to store zero to the number of // * desired colors. 16 bits/cell is plenty for that too.) // * Since the JPEG code is intended to run in small memory model on 80x86 // * machines, we can't just allocate the histogram in one chunk. Instead // * of a true 3-D array, we use a row of pointers to 2-D arrays. Each // * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and // * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that // * on 80x86 machines, the pointer row is in near memory but the actual // * arrays are in far memory (same arrangement as we use for image arrays). #define MAXNUMCOLORS (MAXJSAMPLE+1) // maximum size of colormap // These will do the right thing for either R,G,B or B,G,R color order, // * but you may not like the results for other color orders. #define HIST_C0_BITS 5 // bits of precision in R/B histogram #define HIST_C1_BITS 6 // bits of precision in G histogram #define HIST_C2_BITS 5 // bits of precision in B/R histogram // Number of elements along histogram axes. #define HIST_C0_ELEMS (1<cquantize; register JSAMPROW ptr; register histptr histp; register hist3d histogram = cquantize->histogram; int row; JDIMENSION col; JDIMENSION width = cinfo->output_width; for (row = 0; row < num_rows; row++) { ptr = input_buf[row]; for (col = width; col > 0; col--) { // get pixel value and index into the histogram histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT] [GETJSAMPLE(ptr[1]) >> C1_SHIFT] [GETJSAMPLE(ptr[2]) >> C2_SHIFT]; // increment, check for overflow and undo increment if so. if (++(*histp) <= 0) (*histp)--; ptr += 3; } } } // * Next we have the really interesting routines: selection of a colormap // * given the completed histogram. // * These routines work with a list of "boxes", each representing a rectangular // * subset of the input color space (to histogram precision). typedef struct { // The bounds of the box (inclusive); expressed as histogram indexes int c0min, c0max; int c1min, c1max; int c2min, c2max; // The volume (actually 2-norm) of the box INT32 volume; // The number of nonzero histogram cells within this box LONG colorcount; } box; typedef box * boxptr; LOCAL boxptr find_biggest_color_pop (boxptr boxlist, int numboxes) // Find the splittable box with the largest color population // Returns NULL if no splittable boxes remain { register boxptr boxp; register int i; register LONG maxc = 0; boxptr which = NULL; for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { if (boxp->colorcount > maxc && boxp->volume > 0) { which = boxp; maxc = boxp->colorcount; } } return which; } LOCAL boxptr find_biggest_volume (boxptr boxlist, int numboxes) // Find the splittable box with the largest (scaled) volume // Returns NULL if no splittable boxes remain { register boxptr boxp; register int i; register INT32 maxv = 0; boxptr which = NULL; for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { if (boxp->volume > maxv) { which = boxp; maxv = boxp->volume; } } return which; } LOCAL void update_box (j_decompress_ptr cinfo, boxptr boxp) // Shrink the min/max bounds of a box to enclose only nonzero elements, // and recompute its volume and population { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; hist3d histogram = cquantize->histogram; histptr histp; int c0,c1,c2; int c0min,c0max,c1min,c1max,c2min,c2max; INT32 dist0,dist1,dist2; LONG ccount; c0min = boxp->c0min; c0max = boxp->c0max; c1min = boxp->c1min; c1max = boxp->c1max; c2min = boxp->c2min; c2max = boxp->c2max; if (c0max > c0min) for (c0 = c0min; c0 <= c0max; c0++) for (c1 = c1min; c1 <= c1max; c1++) { histp = & histogram[c0][c1][c2min]; for (c2 = c2min; c2 <= c2max; c2++) if (*histp++ != 0) { boxp->c0min = c0min = c0; goto have_c0min; } } have_c0min: if (c0max > c0min) for (c0 = c0max; c0 >= c0min; c0--) for (c1 = c1min; c1 <= c1max; c1++) { histp = & histogram[c0][c1][c2min]; for (c2 = c2min; c2 <= c2max; c2++) if (*histp++ != 0) { boxp->c0max = c0max = c0; goto have_c0max; } } have_c0max: if (c1max > c1min) for (c1 = c1min; c1 <= c1max; c1++) for (c0 = c0min; c0 <= c0max; c0++) { histp = & histogram[c0][c1][c2min]; for (c2 = c2min; c2 <= c2max; c2++) if (*histp++ != 0) { boxp->c1min = c1min = c1; goto have_c1min; } } have_c1min: if (c1max > c1min) for (c1 = c1max; c1 >= c1min; c1--) for (c0 = c0min; c0 <= c0max; c0++) { histp = & histogram[c0][c1][c2min]; for (c2 = c2min; c2 <= c2max; c2++) if (*histp++ != 0) { boxp->c1max = c1max = c1; goto have_c1max; } } have_c1max: if (c2max > c2min) for (c2 = c2min; c2 <= c2max; c2++) for (c0 = c0min; c0 <= c0max; c0++) { histp = & histogram[c0][c1min][c2]; for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) if (*histp != 0) { boxp->c2min = c2min = c2; goto have_c2min; } } have_c2min: if (c2max > c2min) for (c2 = c2max; c2 >= c2min; c2--) for (c0 = c0min; c0 <= c0max; c0++) { histp = & histogram[c0][c1min][c2]; for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) if (*histp != 0) { boxp->c2max = c2max = c2; goto have_c2max; } } have_c2max: // Update box volume. // * We use 2-norm rather than real volume here; this biases the method // * against making long narrow boxes, and it has the side benefit that // * a box is splittable iff norm > 0. // * Since the differences are expressed in histogram-cell units, // * we have to shift back to JSAMPLE units to get consistent distances; // * after which, we scale according to the selected distance scale factors. dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2; // Now scan remaining volume of box and compute population ccount = 0; for (c0 = c0min; c0 <= c0max; c0++) for (c1 = c1min; c1 <= c1max; c1++) { histp = & histogram[c0][c1][c2min]; for (c2 = c2min; c2 <= c2max; c2++, histp++) if (*histp != 0) { ccount++; } } boxp->colorcount = ccount; } LOCAL int median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes, int desired_colors) // Repeatedly select and split the largest box until we have enough boxes { int n,lb; int c0,c1,c2,cmax; register boxptr b1,b2; while (numboxes < desired_colors) { // Select box to split. // * Current algorithm: by population for first half, then by volume. if (numboxes*2 <= desired_colors) { b1 = find_biggest_color_pop(boxlist, numboxes); } else { b1 = find_biggest_volume(boxlist, numboxes); } if (b1 == NULL) // no splittable boxes left! break; b2 = &boxlist[numboxes]; // where new box will go // Copy the color bounds to the new box. b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max; b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min; // Choose which axis to split the box on. // * Current algorithm: longest scaled axis. // * See notes in update_box about scaling distances. c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE; c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE; c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE; // We want to break any ties in favor of green, then red, blue last. // * This code does the right thing for R,G,B or B,G,R color orders only. #if RGB_RED == 0 cmax = c1; n = 1; if (c0 > cmax) { cmax = c0; n = 0; } if (c2 > cmax) { n = 2; } #else cmax = c1; n = 1; if (c2 > cmax) { cmax = c2; n = 2; } if (c0 > cmax) { n = 0; } #endif // Choose split point along selected axis, and update box bounds. // * Current algorithm: split at halfway point. // * (Since the box has been shrunk to minimum volume, // * any split will produce two nonempty subboxes.) // * Note that lb value is max for lower box, so must be < old max. switch (n) { case 0: lb = (b1->c0max + b1->c0min) / 2; b1->c0max = lb; b2->c0min = lb+1; break; case 1: lb = (b1->c1max + b1->c1min) / 2; b1->c1max = lb; b2->c1min = lb+1; break; case 2: lb = (b1->c2max + b1->c2min) / 2; b1->c2max = lb; b2->c2min = lb+1; break; } // Update stats for boxes update_box(cinfo, b1); update_box(cinfo, b2); numboxes++; } return numboxes; } LOCAL void compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor) // Compute representative color for a box, put it in colormap[icolor] { // Current algorithm: mean weighted by pixels (not colors) // Note it is important to get the rounding correct! my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; hist3d histogram = cquantize->histogram; histptr histp; int c0,c1,c2; int c0min,c0max,c1min,c1max,c2min,c2max; LONG count; LONG total = 0; LONG c0total = 0; LONG c1total = 0; LONG c2total = 0; c0min = boxp->c0min; c0max = boxp->c0max; c1min = boxp->c1min; c1max = boxp->c1max; c2min = boxp->c2min; c2max = boxp->c2max; for (c0 = c0min; c0 <= c0max; c0++) for (c1 = c1min; c1 <= c1max; c1++) { histp = & histogram[c0][c1][c2min]; for (c2 = c2min; c2 <= c2max; c2++) { if ((count = *histp++) != 0) { total += count; c0total += ((c0 << C0_SHIFT) + ((1<>1)) * count; c1total += ((c1 << C1_SHIFT) + ((1<>1)) * count; c2total += ((c2 << C2_SHIFT) + ((1<>1)) * count; } } } cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); } LOCAL void select_colors (j_decompress_ptr cinfo, int desired_colors) // Master routine for color selection { boxptr boxlist; int numboxes; int i; // Allocate workspace for box list boxlist = (boxptr) (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box)); // Initialize one box containing whole space numboxes = 1; boxlist[0].c0min = 0; boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; boxlist[0].c1min = 0; boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; boxlist[0].c2min = 0; boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; // Shrink it to actually-used volume and set its statistics update_box(cinfo, & boxlist[0]); // Perform median-cut to produce final box list numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors); // Compute the representative color for each box, fill colormap for (i = 0; i < numboxes; i++) compute_color(cinfo, & boxlist[i], i); cinfo->actual_number_of_colors = numboxes; TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes); } // * These routines are concerned with the time-critical task of mapping input // * colors to the nearest color in the selected colormap. // * // * We re-use the histogram space as an "inverse color map", essentially a // * cache for the results of nearest-color searches. All colors within a // * histogram cell will be mapped to the same colormap entry, namely the one // * closest to the cell's center. This may not be quite the closest entry to // * the actual input color, but it's almost as good. A zero in the cache // * indicates we haven't found the nearest color for that cell yet; the array // * is cleared to zeroes before starting the mapping pass. When we find the // * nearest color for a cell, its colormap index plus one is recorded in the // * cache for future use. The pass2 scanning routines call fill_inverse_cmap // * when they need to use an unfilled entry in the cache. // * // * Our method of efficiently finding nearest colors is based on the "locally // * sorted search" idea described by Heckbert and on the incremental distance // * calculation described by Spencer W. Thomas in chapter III.1 of Graphics // * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that // * the distances from a given colormap entry to each cell of the histogram can // * be computed quickly using an incremental method: the differences between // * distances to adjacent cells themselves differ by a constant. This allows a // * fairly fast implementation of the "brute force" approach of computing the // * distance from every colormap entry to every histogram cell. Unfortunately, // * it needs a work array to hold the best-distance-so-far for each histogram // * cell (because the inner loop has to be over cells, not colormap entries). // * The work array elements have to be INT32s, so the work array would need // * 256Kb at our recommended precision. This is not feasible in DOS machines. // * // * To get around these problems, we apply Thomas' method to compute the // * nearest colors for only the cells within a small subbox of the histogram. // * The work array need be only as big as the subbox, so the memory usage // * problem is solved. Furthermore, we need not fill subboxes that are never // * referenced in pass2; many images use only part of the color gamut, so a // * fair amount of work is saved. An additional advantage of this // * approach is that we can apply Heckbert's locality criterion to quickly // * eliminate colormap entries that are far away from the subbox; typically // * three-fourths of the colormap entries are rejected by Heckbert's criterion, // * and we need not compute their distances to individual cells in the subbox. // * The speed of this approach is heavily influenced by the subbox size: too // * small means too much overhead, too big loses because Heckbert's criterion // * can't eliminate as many colormap entries. Empirically the best subbox // * size seems to be about 1/512th of the histogram (1/8th in each direction). // * // * Thomas' article also describes a refined method which is asymptotically // * faster than the brute-force method, but it is also far more complex and // * cannot efficiently be applied to small subboxes. It is therefore not // * useful for programs intended to be portable to DOS machines. On machines // * with plenty of memory, filling the whole histogram in one shot with Thomas' // * refined method might be faster than the present code --- but then again, // * it might not be any faster, and it's certainly more complicated. // log2(histogram cells in update box) for each axis; this can be adjusted #define BOX_C0_LOG (HIST_C0_BITS-3) #define BOX_C1_LOG (HIST_C1_BITS-3) #define BOX_C2_LOG (HIST_C2_BITS-3) #define BOX_C0_ELEMS (1<actual_number_of_colors; int maxc0, maxc1, maxc2; int centerc0, centerc1, centerc2; int i, x, ncolors; INT32 minmaxdist, min_dist, max_dist, tdist; INT32 mindist[MAXNUMCOLORS]; // min distance to colormap entry i // Compute true coordinates of update box's upper corner and center. // * Actually we compute the coordinates of the center of the upper-corner // * histogram cell, which are the upper bounds of the volume we care about. // * Note that since ">>" rounds down, the "center" values may be closer to // * min than to max; hence comparisons to them must be "<=", not "<". maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT)); centerc0 = (minc0 + maxc0) >> 1; maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT)); centerc1 = (minc1 + maxc1) >> 1; maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT)); centerc2 = (minc2 + maxc2) >> 1; // For each color in colormap, find: // * 1. its minimum squared-distance to any point in the update box // * (zero if color is within update box); // * 2. its maximum squared-distance to any point in the update box. // * Both of these can be found by considering only the corners of the box. // * We save the minimum distance for each color in mindist[]; // * only the smallest maximum distance is of interest. minmaxdist = 0x7FFFFFFFL; for (i = 0; i < numcolors; i++) { // We compute the squared-c0-distance term, then add in the other two. x = GETJSAMPLE(cinfo->colormap[0][i]); if (x < minc0) { tdist = (x - minc0) * C0_SCALE; min_dist = tdist*tdist; tdist = (x - maxc0) * C0_SCALE; max_dist = tdist*tdist; } else if (x > maxc0) { tdist = (x - maxc0) * C0_SCALE; min_dist = tdist*tdist; tdist = (x - minc0) * C0_SCALE; max_dist = tdist*tdist; } else { // within cell range so no contribution to min_dist min_dist = 0; if (x <= centerc0) { tdist = (x - maxc0) * C0_SCALE; max_dist = tdist*tdist; } else { tdist = (x - minc0) * C0_SCALE; max_dist = tdist*tdist; } } x = GETJSAMPLE(cinfo->colormap[1][i]); if (x < minc1) { tdist = (x - minc1) * C1_SCALE; min_dist += tdist*tdist; tdist = (x - maxc1) * C1_SCALE; max_dist += tdist*tdist; } else if (x > maxc1) { tdist = (x - maxc1) * C1_SCALE; min_dist += tdist*tdist; tdist = (x - minc1) * C1_SCALE; max_dist += tdist*tdist; } else { // within cell range so no contribution to min_dist if (x <= centerc1) { tdist = (x - maxc1) * C1_SCALE; max_dist += tdist*tdist; } else { tdist = (x - minc1) * C1_SCALE; max_dist += tdist*tdist; } } x = GETJSAMPLE(cinfo->colormap[2][i]); if (x < minc2) { tdist = (x - minc2) * C2_SCALE; min_dist += tdist*tdist; tdist = (x - maxc2) * C2_SCALE; max_dist += tdist*tdist; } else if (x > maxc2) { tdist = (x - maxc2) * C2_SCALE; min_dist += tdist*tdist; tdist = (x - minc2) * C2_SCALE; max_dist += tdist*tdist; } else { // within cell range so no contribution to min_dist if (x <= centerc2) { tdist = (x - maxc2) * C2_SCALE; max_dist += tdist*tdist; } else { tdist = (x - minc2) * C2_SCALE; max_dist += tdist*tdist; } } mindist[i] = min_dist; // save away the results if (max_dist < minmaxdist) minmaxdist = max_dist; } // Now we know that no cell in the update box is more than minmaxdist // * away from some colormap entry. Therefore, only colors that are // * within minmaxdist of some part of the box need be considered. ncolors = 0; for (i = 0; i < numcolors; i++) { if (mindist[i] <= minmaxdist) colorlist[ncolors++] = (JSAMPLE) i; } return ncolors; } LOCAL void find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[]) // Find the closest colormap entry for each cell in the update box, // * given the list of candidate colors prepared by find_nearby_colors. // * Return the indexes of the closest entries in the bestcolor[] array. // * This routine uses Thomas' incremental distance calculation method to // * find the distance from a colormap entry to successive cells in the box. { int ic0, ic1, ic2; int i, icolor; register INT32 * bptr; // pointer into bestdist[] array JSAMPLE * cptr; // pointer into bestcolor[] array INT32 dist0, dist1; // initial distance values register INT32 dist2; // current distance in inner loop INT32 xx0, xx1; // distance increments register INT32 xx2; INT32 inc0, inc1, inc2; // initial values for increments // This array holds the distance to the nearest-so-far color for each cell INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; // Initialize best-distance for each cell of the update box bptr = bestdist; for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--) *bptr++ = 0x7FFFFFFFL; // For each color selected by find_nearby_colors, // * compute its distance to the center of each cell in the box. // * If that's less than best-so-far, update best distance and color number. // Nominal steps between cell centers ("x" in Thomas article) #define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) #define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) #define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) for (i = 0; i < numcolors; i++) { icolor = GETJSAMPLE(colorlist[i]); // Compute (square of) distance from minc0/c1/c2 to this color inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE; dist0 = inc0*inc0; inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE; dist0 += inc1*inc1; inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE; dist0 += inc2*inc2; // Form the initial difference increments inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; // Now loop over all cells in box, updating distance per Thomas method bptr = bestdist; cptr = bestcolor; xx0 = inc0; for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) { dist1 = dist0; xx1 = inc1; for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) { dist2 = dist1; xx2 = inc2; for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) { if (dist2 < *bptr) { *bptr = dist2; *cptr = (JSAMPLE) icolor; } dist2 += xx2; xx2 += 2 * STEP_C2 * STEP_C2; bptr++; cptr++; } dist1 += xx1; xx1 += 2 * STEP_C1 * STEP_C1; } dist0 += xx0; xx0 += 2 * STEP_C0 * STEP_C0; } } } LOCAL void fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2) // Fill the inverse-colormap entries in the update box that contains // histogram cell c0/c1/c2. (Only that one cell MUST be filled, but // we can fill as many others as we wish.) { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; hist3d histogram = cquantize->histogram; int minc0, minc1, minc2; // lower left corner of update box int ic0, ic1, ic2; register JSAMPLE * cptr; // pointer into bestcolor[] array register histptr cachep; // pointer into main cache array // This array lists the candidate colormap indexes. JSAMPLE colorlist[MAXNUMCOLORS]; int numcolors; // number of candidate colors // This array holds the actually closest colormap index for each cell. JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; // Convert cell coordinates to update box ID c0 >>= BOX_C0_LOG; c1 >>= BOX_C1_LOG; c2 >>= BOX_C2_LOG; // Compute true coordinates of update box's origin corner. // * Actually we compute the coordinates of the center of the corner // * histogram cell, which are the lower bounds of the volume we care about. minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); // Determine which colormap entries are close enough to be candidates // * for the nearest entry to some cell in the update box. numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist); // Determine the actually nearest colors. find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist, bestcolor); // Save the best color numbers (plus 1) in the main cache array c0 <<= BOX_C0_LOG; // convert ID back to base cell indexes c1 <<= BOX_C1_LOG; c2 <<= BOX_C2_LOG; cptr = bestcolor; for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) { for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) { cachep = & histogram[c0+ic0][c1+ic1][c2]; for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) { *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); } } } } // * Map some rows of pixels to the output colormapped representation. METHODDEF void pass2_no_dither (j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) // This version performs no dithering { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; hist3d histogram = cquantize->histogram; register JSAMPROW inptr, outptr; register histptr cachep; register int c0, c1, c2; int row; JDIMENSION col; JDIMENSION width = cinfo->output_width; for (row = 0; row < num_rows; row++) { inptr = input_buf[row]; outptr = output_buf[row]; for (col = width; col > 0; col--) { // get pixel value and index into the cache c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT; c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT; c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT; cachep = & histogram[c0][c1][c2]; // If we have not seen this color before, find nearest colormap entry // and update the cache if (*cachep == 0) fill_inverse_cmap(cinfo, c0,c1,c2); // Now emit the colormap index for this cell *outptr++ = (JSAMPLE) (*cachep - 1); } } } METHODDEF void pass2_fs_dither (j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) // This version performs Floyd-Steinberg dithering { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; hist3d histogram = cquantize->histogram; register LOCFSERROR cur0, cur1, cur2; // current error or pixel value LOCFSERROR belowerr0, belowerr1, belowerr2; // error for pixel below cur LOCFSERROR bpreverr0, bpreverr1, bpreverr2; // error for below/prev col register FSERRPTR errorptr; // => fserrors[] at column before current JSAMPROW inptr; // => current input pixel JSAMPROW outptr; // => current output pixel histptr cachep; int dir; // +1 or -1 depending on direction int dir3; // 3*dir, for advancing inptr & errorptr int row; JDIMENSION col; JDIMENSION width = cinfo->output_width; JSAMPLE *range_limit = cinfo->sample_range_limit; int *error_limit = cquantize->error_limiter; JSAMPROW colormap0 = cinfo->colormap[0]; JSAMPROW colormap1 = cinfo->colormap[1]; JSAMPROW colormap2 = cinfo->colormap[2]; SHIFT_TEMPS for (row = 0; row < num_rows; row++) { inptr = input_buf[row]; outptr = output_buf[row]; if (cquantize->on_odd_row) { // work right to left in this row inptr += (width-1) * 3; // so point to rightmost pixel outptr += width-1; dir = -1; dir3 = -3; errorptr = cquantize->fserrors + (width+1)*3; // => entry after last column cquantize->on_odd_row = FALSE; // flip for next time } else { // work left to right in this row dir = 1; dir3 = 3; errorptr = cquantize->fserrors; // => entry before first real column cquantize->on_odd_row = TRUE; // flip for next time } // Preset error values: no error propagated to first pixel from left cur0 = cur1 = cur2 = 0; // and no error propagated to row below yet belowerr0 = belowerr1 = belowerr2 = 0; bpreverr0 = bpreverr1 = bpreverr2 = 0; for (col = width; col > 0; col--) { // curN holds the error propagated from the previous pixel on the // * current line. Add the error propagated from the previous line // * to form the complete error correction term for this pixel, and // * round the error term (which is expressed * 16) to an integer. // * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct // * for either sign of the error value. // * Note: errorptr points to *previous* column's array entry. cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4); cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4); cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4); // Limit the error using transfer function set by init_error_limit. // * See comments with init_error_limit for rationale. cur0 = error_limit[cur0]; cur1 = error_limit[cur1]; cur2 = error_limit[cur2]; // Form pixel value + error, and range-limit to 0..MAXJSAMPLE. // * The maximum error is +- MAXJSAMPLE (or less with error limiting); // * this sets the required size of the range_limit array. cur0 += GETJSAMPLE(inptr[0]); cur1 += GETJSAMPLE(inptr[1]); cur2 += GETJSAMPLE(inptr[2]); cur0 = GETJSAMPLE(range_limit[cur0]); cur1 = GETJSAMPLE(range_limit[cur1]); cur2 = GETJSAMPLE(range_limit[cur2]); // Index into the cache with adjusted pixel value cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT]; // If we have not seen this color before, find nearest colormap // entry and update the cache if (*cachep == 0) fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT); // Now emit the colormap index for this cell { register int pixcode = *cachep - 1; *outptr = (JSAMPLE) pixcode; // Compute representation error for this pixel cur0 -= GETJSAMPLE(colormap0[pixcode]); cur1 -= GETJSAMPLE(colormap1[pixcode]); cur2 -= GETJSAMPLE(colormap2[pixcode]); } // Compute error fractions to be propagated to adjacent pixels. // * Add these into the running sums, and simultaneously shift the // * next-line error sums left by 1 column. { register LOCFSERROR bnexterr, delta; bnexterr = cur0; // Process component 0 delta = cur0 * 2; cur0 += delta; // form error * 3 errorptr[0] = (FSERROR) (bpreverr0 + cur0); cur0 += delta; // form error * 5 bpreverr0 = belowerr0 + cur0; belowerr0 = bnexterr; cur0 += delta; // form error * 7 bnexterr = cur1; // Process component 1 delta = cur1 * 2; cur1 += delta; // form error * 3 errorptr[1] = (FSERROR) (bpreverr1 + cur1); cur1 += delta; // form error * 5 bpreverr1 = belowerr1 + cur1; belowerr1 = bnexterr; cur1 += delta; // form error * 7 bnexterr = cur2; // Process component 2 delta = cur2 * 2; cur2 += delta; // form error * 3 errorptr[2] = (FSERROR) (bpreverr2 + cur2); cur2 += delta; // form error * 5 bpreverr2 = belowerr2 + cur2; belowerr2 = bnexterr; cur2 += delta; // form error * 7 } // At this point curN contains the 7/16 error value to be propagated // * to the next pixel on the current line, and all the errors for the // * next line have been shifted over. We are therefore ready to move on. inptr += dir3; // Advance pixel pointers to next column outptr += dir; errorptr += dir3; // advance errorptr to current column } // Post-loop cleanup: we must unload the final error values into the // * final fserrors[] entry. Note we need not unload belowerrN because // * it is for the dummy column before or after the actual array. errorptr[0] = (FSERROR) bpreverr0; // unload prev errs into array errorptr[1] = (FSERROR) bpreverr1; errorptr[2] = (FSERROR) bpreverr2; } } // * Initialize the error-limiting transfer function (lookup table). // * The raw F-S error computation can potentially compute error values of up to // * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be // * much less, otherwise obviously wrong pixels will be created. (Typical // * effects include weird fringes at color-area boundaries, isolated bright // * pixels in a dark area, etc.) The standard advice for avoiding this problem // * is to ensure that the "corners" of the color cube are allocated as output // * colors; then repeated errors in the same direction cannot cause cascading // * error buildup. However, that only prevents the error from getting // * completely out of hand; Aaron Giles reports that error limiting improves // * the results even with corner colors allocated. // * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty // * well, but the smoother transfer function used below is even better. Thanks // * to Aaron Giles for this idea. LOCAL void init_error_limit (j_decompress_ptr cinfo) // Allocate and fill in the error_limiter table { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; int * table; int in, out; table = (int *) (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int)); table += MAXJSAMPLE; // so can index -MAXJSAMPLE .. +MAXJSAMPLE cquantize->error_limiter = table; #define STEPSIZE ((MAXJSAMPLE+1)/16) // Map errors 1:1 up to +- MAXJSAMPLE/16 out = 0; for (in = 0; in < STEPSIZE; in++, out++) { table[in] = out; table[-in] = -out; } // Map errors 1:2 up to +- 3*MAXJSAMPLE/16 for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) { table[in] = out; table[-in] = -out; } // Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) for (; in <= MAXJSAMPLE; in++) { table[in] = out; table[-in] = -out; } #undef STEPSIZE } // * Finish up at the end of each pass. METHODDEF void finish_pass1 (j_decompress_ptr cinfo) { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; // Select the representative colors and fill in cinfo->colormap cinfo->colormap = cquantize->sv_colormap; select_colors(cinfo, cquantize->desired); // Force next pass to zero the color index table cquantize->needs_zeroed = TRUE; } METHODDEF void finish_pass2 (j_decompress_ptr cinfo) { // no work } // * Initialize for each processing pass. METHODDEF void start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan) { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; hist3d histogram = cquantize->histogram; int i; // Only F-S dithering or no dithering is supported. // If user asks for ordered dither, give him F-S. if (cinfo->dither_mode != JDITHER_NONE) cinfo->dither_mode = JDITHER_FS; if (is_pre_scan) { // Set up method pointers cquantize->pub.color_quantize = prescan_quantize; cquantize->pub.finish_pass = finish_pass1; cquantize->needs_zeroed = TRUE; // Always zero histogram } else { // Set up method pointers if (cinfo->dither_mode == JDITHER_FS) cquantize->pub.color_quantize = pass2_fs_dither; else cquantize->pub.color_quantize = pass2_no_dither; cquantize->pub.finish_pass = finish_pass2; // Make sure color count is acceptable i = cinfo->actual_number_of_colors; if (i < 1) ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1); if (i > MAXNUMCOLORS) ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); if (cinfo->dither_mode == JDITHER_FS) { size_t arraysize = (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))); // Allocate Floyd-Steinberg workspace if we didn't already. if (cquantize->fserrors == NULL) cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize); // Initialize the propagated errors to zero. jzero_far((void FAR *) cquantize->fserrors, arraysize); // Make the error-limit table if we didn't already. if (cquantize->error_limiter == NULL) init_error_limit(cinfo); cquantize->on_odd_row = FALSE; } } // Zero the histogram or inverse color map, if necessary if (cquantize->needs_zeroed) { for (i = 0; i < HIST_C0_ELEMS; i++) { jzero_far((void FAR *) histogram[i], HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); } cquantize->needs_zeroed = FALSE; } } // * Switch to a new external colormap between output passes. METHODDEF void new_color_map_2_quant (j_decompress_ptr cinfo) { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; // Reset the inverse color map cquantize->needs_zeroed = TRUE; } // * Module initialization routine for 2-pass color quantization. GLOBAL void jinit_2pass_quantizer (j_decompress_ptr cinfo) { my_cquantize_ptr cquantize; int i; cquantize = (my_cquantize_ptr) (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, SIZEOF(my_cquantizer)); cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; cquantize->pub.start_pass = start_pass_2_quant; cquantize->pub.new_color_map = new_color_map_2_quant; cquantize->fserrors = NULL; // flag optional arrays not allocated cquantize->error_limiter = NULL; // Make sure jdmaster didn't give me a case I can't handle if (cinfo->out_color_components != 3) ERREXIT(cinfo, JERR_NOTIMPL); // Allocate the histogram/inverse colormap storage cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d)); for (i = 0; i < HIST_C0_ELEMS; i++) { cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large) ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); } cquantize->needs_zeroed = TRUE; // histogram is garbage now // Allocate storage for the completed colormap, if required. // * We do this now since it is FAR storage and may affect // * the memory manager's space calculations. if (cinfo->enable_2pass_quant) { // Make sure color count is acceptable int desired = cinfo->desired_number_of_colors; // Lower bound on # of colors ... somewhat arbitrary as long as > 0 if (desired < 8) ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8); // Make sure colormap indexes can be represented by JSAMPLEs if (desired > MAXNUMCOLORS) ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); cquantize->sv_colormap = (*cinfo->mem->alloc_sarray) ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3); cquantize->desired = desired; } else cquantize->sv_colormap = NULL; // Only F-S dithering or no dithering is supported. // If user asks for ordered dither, give him F-S. if (cinfo->dither_mode != JDITHER_NONE) cinfo->dither_mode = JDITHER_FS; // Allocate Floyd-Steinberg workspace if necessary. // * This isn't really needed until pass 2, but again it is FAR storage. // * Although we will cope with a later change in dither_mode, // * we do not promise to honor max_memory_to_use if dither_mode changes. if (cinfo->dither_mode == JDITHER_FS) { cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) ((j_common_ptr) cinfo, JPOOL_IMAGE, (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR)))); // Might as well create the error-limiting table too. init_error_limit(cinfo); } } #endif // QUANT_2PASS_SUPPORTED