/* * 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<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<>1)) * count; c1total += ((c1 << C_SHIFT) + ((1<>1)) * count; c2total += ((c2 << C_SHIFT) + ((1<>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<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 */