/* * 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 << 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 */ typedef struct { struct jpeg_color_quantizer pub;/* public fields */ /* Space for the eventually created colormap is stashed here */ JSAMPARRAY sv_colormap; /* colormap allocated at init time */ int desired; /* desired # of colors = size of colormap */ /* Variables for accumulating image statistics */ hist3d histogram; /* pointer to the histogram */ boolean needs_zeroed; /* TRUE if next pass must zero histogram */ /* Variables for Floyd-Steinberg dithering */ FSERRPTR fserrors; /* accumulated errors */ boolean on_odd_row; /* flag to remember which row we are on */ int * error_limiter; /* table for clamping the applied error */ } my_cquantizer; typedef my_cquantizer * my_cquantize_ptr; /* * Prescan some rows of pixels. * In this module the prescan simply updates the histogram, which has been * initialized to zeroes by start_pass. * An output_buf parameter is required by the method signature, but no data * is actually output (in fact the buffer controller is probably passing a * NULL pointer). */ METHODDEF void prescan_quantize( j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows ) { my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; register JSAMPROW ptr; register histptr histp; register hist3d histogram = cquantize->histogram; int row; JDIMENSION col; JDIMENSION width = cinfo->output_width; for ( row = 0; row < num_rows; row++ ) { ptr = input_buf[row]; for ( col = width; col > 0; col-- ) { /* get pixel value and index into the histogram */ histp = &histogram[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 << C0_SHIFT ) >> 1 ) ) * count; c1total += ( ( c1 << C1_SHIFT ) + ( ( 1 << C1_SHIFT ) >> 1 ) ) * count; c2total += ( ( c2 << C2_SHIFT ) + ( ( 1 << C2_SHIFT ) >> 1 ) ) * count; } } } } cinfo->colormap[0][icolor] = (JSAMPLE) ( ( c0total + ( total >> 1 ) ) / total ); cinfo->colormap[1][icolor] = (JSAMPLE) ( ( c1total + ( total >> 1 ) ) / total ); cinfo->colormap[2][icolor] = (JSAMPLE) ( ( c2total + ( total >> 1 ) ) / total ); } LOCAL void select_colors( j_decompress_ptr cinfo, int desired_colors ) { /* Master routine for color selection */ boxptr boxlist; int numboxes; int i; /* Allocate workspace for box list */ boxlist = (boxptr) ( *cinfo->mem->alloc_small ) ( (j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF( box ) ); /* Initialize one box containing whole space */ numboxes = 1; boxlist[0].c0min = 0; boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; boxlist[0].c1min = 0; boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; boxlist[0].c2min = 0; boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; /* Shrink it to actually-used volume and set its statistics */ update_box( cinfo, &boxlist[0] ); /* Perform median-cut to produce final box list */ numboxes = median_cut( cinfo, boxlist, numboxes, desired_colors ); /* Compute the representative color for each box, fill colormap */ for ( i = 0; i < numboxes; i++ ) { compute_color( cinfo, &boxlist[i], i ); } cinfo->actual_number_of_colors = numboxes; TRACEMS1( cinfo, 1, JTRC_QUANT_SELECTED, numboxes ); } /* * These routines are concerned with the time-critical task of mapping input * colors to the nearest color in the selected colormap. * * We re-use the histogram space as an "inverse color map", essentially a * cache for the results of nearest-color searches. All colors within a * histogram cell will be mapped to the same colormap entry, namely the one * closest to the cell's center. This may not be quite the closest entry to * the actual input color, but it's almost as good. A zero in the cache * indicates we haven't found the nearest color for that cell yet; the array * is cleared to zeroes before starting the mapping pass. When we find the * nearest color for a cell, its colormap index plus one is recorded in the * cache for future use. The pass2 scanning routines call fill_inverse_cmap * when they need to use an unfilled entry in the cache. * * Our method of efficiently finding nearest colors is based on the "locally * sorted search" idea described by Heckbert and on the incremental distance * calculation described by Spencer W. Thomas in chapter III.1 of Graphics * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that * the distances from a given colormap entry to each cell of the histogram can * be computed quickly using an incremental method: the differences between * distances to adjacent cells themselves differ by a constant. This allows a * fairly fast implementation of the "brute force" approach of computing the * distance from every colormap entry to every histogram cell. Unfortunately, * it needs a work array to hold the best-distance-so-far for each histogram * cell (because the inner loop has to be over cells, not colormap entries). * The work array elements have to be 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; 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 */