| /* |
| * jquant2.c |
| * |
| * This file was part of the Independent JPEG Group's software: |
| * Copyright (C) 1991-1996, Thomas G. Lane. |
| * libjpeg-turbo Modifications: |
| * Copyright (C) 2009, D. R. Commander. |
| * 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 */ |
| |
| static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE}; |
| #define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]] |
| #define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]] |
| #define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]] |
| |
| /* |
| * 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[cinfo->out_color_space] == 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; } |
| } |
| /* 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 */ |