/*
* jquant2.c
*
* Copyright (C) 1991-1996, Thomas G. Lane.
* This file is part of the Independent JPEG Group's software.
* For conditions of distribution and use, see the accompanying README file.
*
* This file contains 2-pass color quantization (color mapping) routines.
* These routines provide selection of a custom color map for an image,
* followed by mapping of the image to that color map, with optional
* Floyd-Steinberg dithering.
* It is also possible to use just the second pass to map to an arbitrary
* externally-given color map.
*
* Note: ordered dithering is not supported, since there isn't any fast
* way to compute intercolor distances; it's unclear that ordered dither's
* fundamental assumptions even hold with an irregularly spaced color map.
*/
#define JPEG_INTERNALS
#include "jinclude.h"
#include "jpeglib.h"
#ifdef QUANT_2PASS_SUPPORTED
/*
* This module implements the well-known Heckbert paradigm for color
* quantization. Most of the ideas used here can be traced back to
* Heckbert's seminal paper
* Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
* Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
*
* In the first pass over the image, we accumulate a histogram showing the
* usage count of each possible color. To keep the histogram to a reasonable
* size, we reduce the precision of the input; typical practice is to retain
* 5 or 6 bits per color, so that 8 or 4 different input values are counted
* in the same histogram cell.
*
* Next, the color-selection step begins with a box representing the whole
* color space, and repeatedly splits the "largest" remaining box until we
* have as many boxes as desired colors. Then the mean color in each
* remaining box becomes one of the possible output colors.
*
* The second pass over the image maps each input pixel to the closest output
* color (optionally after applying a Floyd-Steinberg dithering correction).
* This mapping is logically trivial, but making it go fast enough requires
* considerable care.
*
* Heckbert-style quantizers vary a good deal in their policies for choosing
* the "largest" box and deciding where to cut it. The particular policies
* used here have proved out well in experimental comparisons, but better ones
* may yet be found.
*
* In earlier versions of the IJG code, this module quantized in YCbCr color
* space, processing the raw upsampled data without a color conversion step.
* This allowed the color conversion math to be done only once per colormap
* entry, not once per pixel. However, that optimization precluded other
* useful optimizations (such as merging color conversion with upsampling)
* and it also interfered with desired capabilities such as quantizing to an
* externally-supplied colormap. We have therefore abandoned that approach.
* The present code works in the post-conversion color space, typically RGB.
*
* To improve the visual quality of the results, we actually work in scaled
* RGB space, giving G distances more weight than R, and R in turn more than
* B. To do everything in integer math, we must use integer scale factors.
* The 2/3/1 scale factors used here correspond loosely to the relative
* weights of the colors in the NTSC grayscale equation.
* If you want to use this code to quantize a non-RGB color space, you'll
* probably need to change these scale factors.
*/
#define R_SCALE 2 /* scale R distances by this much */
#define G_SCALE 3 /* scale G distances by this much */
#define B_SCALE 1 /* and B by this much */
/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
* in jmorecfg.h. As the code stands, it will do the right thing for R,G,B
* and B,G,R orders. If you define some other weird order in jmorecfg.h,
* you'll get compile errors until you extend this logic. In that case
* you'll probably want to tweak the histogram sizes too.
*/
#if RGB_RED == 0
#define C0_SCALE R_SCALE
#endif
#if RGB_BLUE == 0
#define C0_SCALE B_SCALE
#endif
#if RGB_GREEN == 1
#define C1_SCALE G_SCALE
#endif
#if RGB_RED == 2
#define C2_SCALE R_SCALE
#endif
#if RGB_BLUE == 2
#define C2_SCALE B_SCALE
#endif
/*
* First we have the histogram data structure and routines for creating it.
*
* The number of bits of precision can be adjusted by changing these symbols.
* We recommend keeping 6 bits for G and 5 each for R and B.
* If you have plenty of memory and cycles, 6 bits all around gives marginally
* better results; if you are short of memory, 5 bits all around will save
* some space but degrade the results.
* To maintain a fully accurate histogram, we'd need to allocate a "long"
* (preferably unsigned long) for each cell. In practice this is overkill;
* we can get by with 16 bits per cell. Few of the cell counts will overflow,
* and clamping those that do overflow to the maximum value will give close-
* enough results. This reduces the recommended histogram size from 256Kb
* to 128Kb, which is a useful savings on PC-class machines.
* (In the second pass the histogram space is re-used for pixel mapping data;
* in that capacity, each cell must be able to store zero to the number of
* desired colors. 16 bits/cell is plenty for that too.)
* Since the JPEG code is intended to run in small memory model on 80x86
* machines, we can't just allocate the histogram in one chunk. Instead
* of a true 3-D array, we use a row of pointers to 2-D arrays. Each
* pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
* each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
* on 80x86 machines, the pointer row is in near memory but the actual
* arrays are in far memory (same arrangement as we use for image arrays).
*/
#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
/* These will do the right thing for either R,G,B or B,G,R color order,
* but you may not like the results for other color orders.
*/
#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
#define HIST_C1_BITS 6 /* bits of precision in G histogram */
#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
/* Number of elements along histogram axes. */
#define HIST_C0_ELEMS (1<cquantize;
register JSAMPROW ptr;
register histptr histp;
register hist3d histogram = cquantize->histogram;
int row;
JDIMENSION col;
JDIMENSION width = cinfo->output_width;
for (row = 0; row < num_rows; row++) {
ptr = input_buf[row];
for (col = width; col > 0; col--) {
/* get pixel value and index into the histogram */
histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
[GETJSAMPLE(ptr[1]) >> C1_SHIFT]
[GETJSAMPLE(ptr[2]) >> C2_SHIFT];
/* increment, check for overflow and undo increment if so. */
if (++(*histp) <= 0)
(*histp)--;
ptr += 3;
}
}
}
/*
* Next we have the really interesting routines: selection of a colormap
* given the completed histogram.
* These routines work with a list of "boxes", each representing a rectangular
* subset of the input color space (to histogram precision).
*/
typedef struct {
/* The bounds of the box (inclusive); expressed as histogram indexes */
int c0min, c0max;
int c1min, c1max;
int c2min, c2max;
/* The volume (actually 2-norm) of the box */
INT32 volume;
/* The number of nonzero histogram cells within this box */
long colorcount;
} box;
typedef box * boxptr;
LOCAL(boxptr)
find_biggest_color_pop (boxptr boxlist, int numboxes)
/* Find the splittable box with the largest color population */
/* Returns NULL if no splittable boxes remain */
{
register boxptr boxp;
register int i;
register long maxc = 0;
boxptr which = NULL;
for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
if (boxp->colorcount > maxc && boxp->volume > 0) {
which = boxp;
maxc = boxp->colorcount;
}
}
return which;
}
LOCAL(boxptr)
find_biggest_volume (boxptr boxlist, int numboxes)
/* Find the splittable box with the largest (scaled) volume */
/* Returns NULL if no splittable boxes remain */
{
register boxptr boxp;
register int i;
register INT32 maxv = 0;
boxptr which = NULL;
for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
if (boxp->volume > maxv) {
which = boxp;
maxv = boxp->volume;
}
}
return which;
}
LOCAL(void)
update_box (j_decompress_ptr cinfo, boxptr boxp)
/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
/* and recompute its volume and population */
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
hist3d histogram = cquantize->histogram;
histptr histp;
int c0,c1,c2;
int c0min,c0max,c1min,c1max,c2min,c2max;
INT32 dist0,dist1,dist2;
long ccount;
c0min = boxp->c0min; c0max = boxp->c0max;
c1min = boxp->c1min; c1max = boxp->c1max;
c2min = boxp->c2min; c2max = boxp->c2max;
if (c0max > c0min)
for (c0 = c0min; c0 <= c0max; c0++)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c0min = c0min = c0;
goto have_c0min;
}
}
have_c0min:
if (c0max > c0min)
for (c0 = c0max; c0 >= c0min; c0--)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c0max = c0max = c0;
goto have_c0max;
}
}
have_c0max:
if (c1max > c1min)
for (c1 = c1min; c1 <= c1max; c1++)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c1min = c1min = c1;
goto have_c1min;
}
}
have_c1min:
if (c1max > c1min)
for (c1 = c1max; c1 >= c1min; c1--)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c1max = c1max = c1;
goto have_c1max;
}
}
have_c1max:
if (c2max > c2min)
for (c2 = c2min; c2 <= c2max; c2++)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1min][c2];
for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
if (*histp != 0) {
boxp->c2min = c2min = c2;
goto have_c2min;
}
}
have_c2min:
if (c2max > c2min)
for (c2 = c2max; c2 >= c2min; c2--)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1min][c2];
for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
if (*histp != 0) {
boxp->c2max = c2max = c2;
goto have_c2max;
}
}
have_c2max:
/* Update box volume.
* We use 2-norm rather than real volume here; this biases the method
* against making long narrow boxes, and it has the side benefit that
* a box is splittable iff norm > 0.
* Since the differences are expressed in histogram-cell units,
* we have to shift back to JSAMPLE units to get consistent distances;
* after which, we scale according to the selected distance scale factors.
*/
dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
/* Now scan remaining volume of box and compute population */
ccount = 0;
for (c0 = c0min; c0 <= c0max; c0++)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++, histp++)
if (*histp != 0) {
ccount++;
}
}
boxp->colorcount = ccount;
}
LOCAL(int)
median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
int desired_colors)
/* Repeatedly select and split the largest box until we have enough boxes */
{
int n,lb;
int c0,c1,c2,cmax;
register boxptr b1,b2;
while (numboxes < desired_colors) {
/* Select box to split.
* Current algorithm: by population for first half, then by volume.
*/
if (numboxes*2 <= desired_colors) {
b1 = find_biggest_color_pop(boxlist, numboxes);
} else {
b1 = find_biggest_volume(boxlist, numboxes);
}
if (b1 == NULL) /* no splittable boxes left! */
break;
b2 = &boxlist[numboxes]; /* where new box will go */
/* Copy the color bounds to the new box. */
b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
/* Choose which axis to split the box on.
* Current algorithm: longest scaled axis.
* See notes in update_box about scaling distances.
*/
c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
/* We want to break any ties in favor of green, then red, blue last.
* This code does the right thing for R,G,B or B,G,R color orders only.
*/
#if RGB_RED == 0
cmax = c1; n = 1;
if (c0 > cmax) { cmax = c0; n = 0; }
if (c2 > cmax) { n = 2; }
#else
cmax = c1; n = 1;
if (c2 > cmax) { cmax = c2; n = 2; }
if (c0 > cmax) { n = 0; }
#endif
/* Choose split point along selected axis, and update box bounds.
* Current algorithm: split at halfway point.
* (Since the box has been shrunk to minimum volume,
* any split will produce two nonempty subboxes.)
* Note that lb value is max for lower box, so must be < old max.
*/
switch (n) {
case 0:
lb = (b1->c0max + b1->c0min) / 2;
b1->c0max = lb;
b2->c0min = lb+1;
break;
case 1:
lb = (b1->c1max + b1->c1min) / 2;
b1->c1max = lb;
b2->c1min = lb+1;
break;
case 2:
lb = (b1->c2max + b1->c2min) / 2;
b1->c2max = lb;
b2->c2min = lb+1;
break;
}
/* Update stats for boxes */
update_box(cinfo, b1);
update_box(cinfo, b2);
numboxes++;
}
return numboxes;
}
LOCAL(void)
compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
/* Compute representative color for a box, put it in colormap[icolor] */
{
/* Current algorithm: mean weighted by pixels (not colors) */
/* Note it is important to get the rounding correct! */
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
hist3d histogram = cquantize->histogram;
histptr histp;
int c0,c1,c2;
int c0min,c0max,c1min,c1max,c2min,c2max;
long count;
long total = 0;
long c0total = 0;
long c1total = 0;
long c2total = 0;
c0min = boxp->c0min; c0max = boxp->c0max;
c1min = boxp->c1min; c1max = boxp->c1max;
c2min = boxp->c2min; c2max = boxp->c2max;
for (c0 = c0min; c0 <= c0max; c0++)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++) {
if ((count = *histp++) != 0) {
total += count;
c0total += ((c0 << C0_SHIFT) + ((1<>1)) * count;
c1total += ((c1 << C1_SHIFT) + ((1<>1)) * count;
c2total += ((c2 << C2_SHIFT) + ((1<>1)) * count;
}
}
}
cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
}
LOCAL(void)
select_colors (j_decompress_ptr cinfo, int desired_colors)
/* Master routine for color selection */
{
boxptr boxlist;
int numboxes;
int i;
/* Allocate workspace for box list */
boxlist = (boxptr) (*cinfo->mem->alloc_small)
((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
/* Initialize one box containing whole space */
numboxes = 1;
boxlist[0].c0min = 0;
boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
boxlist[0].c1min = 0;
boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
boxlist[0].c2min = 0;
boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
/* Shrink it to actually-used volume and set its statistics */
update_box(cinfo, & boxlist[0]);
/* Perform median-cut to produce final box list */
numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
/* Compute the representative color for each box, fill colormap */
for (i = 0; i < numboxes; i++)
compute_color(cinfo, & boxlist[i], i);
cinfo->actual_number_of_colors = numboxes;
TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
}
/*
* These routines are concerned with the time-critical task of mapping input
* colors to the nearest color in the selected colormap.
*
* We re-use the histogram space as an "inverse color map", essentially a
* cache for the results of nearest-color searches. All colors within a
* histogram cell will be mapped to the same colormap entry, namely the one
* closest to the cell's center. This may not be quite the closest entry to
* the actual input color, but it's almost as good. A zero in the cache
* indicates we haven't found the nearest color for that cell yet; the array
* is cleared to zeroes before starting the mapping pass. When we find the
* nearest color for a cell, its colormap index plus one is recorded in the
* cache for future use. The pass2 scanning routines call fill_inverse_cmap
* when they need to use an unfilled entry in the cache.
*
* Our method of efficiently finding nearest colors is based on the "locally
* sorted search" idea described by Heckbert and on the incremental distance
* calculation described by Spencer W. Thomas in chapter III.1 of Graphics
* Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
* the distances from a given colormap entry to each cell of the histogram can
* be computed quickly using an incremental method: the differences between
* distances to adjacent cells themselves differ by a constant. This allows a
* fairly fast implementation of the "brute force" approach of computing the
* distance from every colormap entry to every histogram cell. Unfortunately,
* it needs a work array to hold the best-distance-so-far for each histogram
* cell (because the inner loop has to be over cells, not colormap entries).
* The work array elements have to be INT32s, so the work array would need
* 256Kb at our recommended precision. This is not feasible in DOS machines.
*
* To get around these problems, we apply Thomas' method to compute the
* nearest colors for only the cells within a small subbox of the histogram.
* The work array need be only as big as the subbox, so the memory usage
* problem is solved. Furthermore, we need not fill subboxes that are never
* referenced in pass2; many images use only part of the color gamut, so a
* fair amount of work is saved. An additional advantage of this
* approach is that we can apply Heckbert's locality criterion to quickly
* eliminate colormap entries that are far away from the subbox; typically
* three-fourths of the colormap entries are rejected by Heckbert's criterion,
* and we need not compute their distances to individual cells in the subbox.
* The speed of this approach is heavily influenced by the subbox size: too
* small means too much overhead, too big loses because Heckbert's criterion
* can't eliminate as many colormap entries. Empirically the best subbox
* size seems to be about 1/512th of the histogram (1/8th in each direction).
*
* Thomas' article also describes a refined method which is asymptotically
* faster than the brute-force method, but it is also far more complex and
* cannot efficiently be applied to small subboxes. It is therefore not
* useful for programs intended to be portable to DOS machines. On machines
* with plenty of memory, filling the whole histogram in one shot with Thomas'
* refined method might be faster than the present code --- but then again,
* it might not be any faster, and it's certainly more complicated.
*/
/* log2(histogram cells in update box) for each axis; this can be adjusted */
#define BOX_C0_LOG (HIST_C0_BITS-3)
#define BOX_C1_LOG (HIST_C1_BITS-3)
#define BOX_C2_LOG (HIST_C2_BITS-3)
#define BOX_C0_ELEMS (1<actual_number_of_colors;
int maxc0, maxc1, maxc2;
int centerc0, centerc1, centerc2;
int i, x, ncolors;
INT32 minmaxdist, min_dist, max_dist, tdist;
INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
/* Compute true coordinates of update box's upper corner and center.
* Actually we compute the coordinates of the center of the upper-corner
* histogram cell, which are the upper bounds of the volume we care about.
* Note that since ">>" rounds down, the "center" values may be closer to
* min than to max; hence comparisons to them must be "<=", not "<".
*/
maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
centerc0 = (minc0 + maxc0) >> 1;
maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
centerc1 = (minc1 + maxc1) >> 1;
maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
centerc2 = (minc2 + maxc2) >> 1;
/* For each color in colormap, find:
* 1. its minimum squared-distance to any point in the update box
* (zero if color is within update box);
* 2. its maximum squared-distance to any point in the update box.
* Both of these can be found by considering only the corners of the box.
* We save the minimum distance for each color in mindist[];
* only the smallest maximum distance is of interest.
*/
minmaxdist = 0x7FFFFFFFL;
for (i = 0; i < numcolors; i++) {
/* We compute the squared-c0-distance term, then add in the other two. */
x = GETJSAMPLE(cinfo->colormap[0][i]);
if (x < minc0) {
tdist = (x - minc0) * C0_SCALE;
min_dist = tdist*tdist;
tdist = (x - maxc0) * C0_SCALE;
max_dist = tdist*tdist;
} else if (x > maxc0) {
tdist = (x - maxc0) * C0_SCALE;
min_dist = tdist*tdist;
tdist = (x - minc0) * C0_SCALE;
max_dist = tdist*tdist;
} else {
/* within cell range so no contribution to min_dist */
min_dist = 0;
if (x <= centerc0) {
tdist = (x - maxc0) * C0_SCALE;
max_dist = tdist*tdist;
} else {
tdist = (x - minc0) * C0_SCALE;
max_dist = tdist*tdist;
}
}
x = GETJSAMPLE(cinfo->colormap[1][i]);
if (x < minc1) {
tdist = (x - minc1) * C1_SCALE;
min_dist += tdist*tdist;
tdist = (x - maxc1) * C1_SCALE;
max_dist += tdist*tdist;
} else if (x > maxc1) {
tdist = (x - maxc1) * C1_SCALE;
min_dist += tdist*tdist;
tdist = (x - minc1) * C1_SCALE;
max_dist += tdist*tdist;
} else {
/* within cell range so no contribution to min_dist */
if (x <= centerc1) {
tdist = (x - maxc1) * C1_SCALE;
max_dist += tdist*tdist;
} else {
tdist = (x - minc1) * C1_SCALE;
max_dist += tdist*tdist;
}
}
x = GETJSAMPLE(cinfo->colormap[2][i]);
if (x < minc2) {
tdist = (x - minc2) * C2_SCALE;
min_dist += tdist*tdist;
tdist = (x - maxc2) * C2_SCALE;
max_dist += tdist*tdist;
} else if (x > maxc2) {
tdist = (x - maxc2) * C2_SCALE;
min_dist += tdist*tdist;
tdist = (x - minc2) * C2_SCALE;
max_dist += tdist*tdist;
} else {
/* within cell range so no contribution to min_dist */
if (x <= centerc2) {
tdist = (x - maxc2) * C2_SCALE;
max_dist += tdist*tdist;
} else {
tdist = (x - minc2) * C2_SCALE;
max_dist += tdist*tdist;
}
}
mindist[i] = min_dist; /* save away the results */
if (max_dist < minmaxdist)
minmaxdist = max_dist;
}
/* Now we know that no cell in the update box is more than minmaxdist
* away from some colormap entry. Therefore, only colors that are
* within minmaxdist of some part of the box need be considered.
*/
ncolors = 0;
for (i = 0; i < numcolors; i++) {
if (mindist[i] <= minmaxdist)
colorlist[ncolors++] = (JSAMPLE) i;
}
return ncolors;
}
LOCAL(void)
find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
/* Find the closest colormap entry for each cell in the update box,
* given the list of candidate colors prepared by find_nearby_colors.
* Return the indexes of the closest entries in the bestcolor[] array.
* This routine uses Thomas' incremental distance calculation method to
* find the distance from a colormap entry to successive cells in the box.
*/
{
int ic0, ic1, ic2;
int i, icolor;
register INT32 * bptr; /* pointer into bestdist[] array */
JSAMPLE * cptr; /* pointer into bestcolor[] array */
INT32 dist0, dist1; /* initial distance values */
register INT32 dist2; /* current distance in inner loop */
INT32 xx0, xx1; /* distance increments */
register INT32 xx2;
INT32 inc0, inc1, inc2; /* initial values for increments */
/* This array holds the distance to the nearest-so-far color for each cell */
INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
/* Initialize best-distance for each cell of the update box */
bptr = bestdist;
for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
*bptr++ = 0x7FFFFFFFL;
/* For each color selected by find_nearby_colors,
* compute its distance to the center of each cell in the box.
* If that's less than best-so-far, update best distance and color number.
*/
/* Nominal steps between cell centers ("x" in Thomas article) */
#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
for (i = 0; i < numcolors; i++) {
icolor = GETJSAMPLE(colorlist[i]);
/* Compute (square of) distance from minc0/c1/c2 to this color */
inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
dist0 = inc0*inc0;
inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
dist0 += inc1*inc1;
inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
dist0 += inc2*inc2;
/* Form the initial difference increments */
inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
/* Now loop over all cells in box, updating distance per Thomas method */
bptr = bestdist;
cptr = bestcolor;
xx0 = inc0;
for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
dist1 = dist0;
xx1 = inc1;
for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
dist2 = dist1;
xx2 = inc2;
for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
if (dist2 < *bptr) {
*bptr = dist2;
*cptr = (JSAMPLE) icolor;
}
dist2 += xx2;
xx2 += 2 * STEP_C2 * STEP_C2;
bptr++;
cptr++;
}
dist1 += xx1;
xx1 += 2 * STEP_C1 * STEP_C1;
}
dist0 += xx0;
xx0 += 2 * STEP_C0 * STEP_C0;
}
}
}
LOCAL(void)
fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
/* Fill the inverse-colormap entries in the update box that contains */
/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
/* we can fill as many others as we wish.) */
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
hist3d histogram = cquantize->histogram;
int minc0, minc1, minc2; /* lower left corner of update box */
int ic0, ic1, ic2;
register JSAMPLE * cptr; /* pointer into bestcolor[] array */
register histptr cachep; /* pointer into main cache array */
/* This array lists the candidate colormap indexes. */
JSAMPLE colorlist[MAXNUMCOLORS];
int numcolors; /* number of candidate colors */
/* This array holds the actually closest colormap index for each cell. */
JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
/* Convert cell coordinates to update box ID */
c0 >>= BOX_C0_LOG;
c1 >>= BOX_C1_LOG;
c2 >>= BOX_C2_LOG;
/* Compute true coordinates of update box's origin corner.
* Actually we compute the coordinates of the center of the corner
* histogram cell, which are the lower bounds of the volume we care about.
*/
minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
/* Determine which colormap entries are close enough to be candidates
* for the nearest entry to some cell in the update box.
*/
numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
/* Determine the actually nearest colors. */
find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
bestcolor);
/* Save the best color numbers (plus 1) in the main cache array */
c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
c1 <<= BOX_C1_LOG;
c2 <<= BOX_C2_LOG;
cptr = bestcolor;
for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
cachep = & histogram[c0+ic0][c1+ic1][c2];
for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
}
}
}
}
/*
* Map some rows of pixels to the output colormapped representation.
*/
METHODDEF(void)
pass2_no_dither (j_decompress_ptr cinfo,
JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
/* This version performs no dithering */
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
hist3d histogram = cquantize->histogram;
register JSAMPROW inptr, outptr;
register histptr cachep;
register int c0, c1, c2;
int row;
JDIMENSION col;
JDIMENSION width = cinfo->output_width;
for (row = 0; row < num_rows; row++) {
inptr = input_buf[row];
outptr = output_buf[row];
for (col = width; col > 0; col--) {
/* get pixel value and index into the cache */
c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
cachep = & histogram[c0][c1][c2];
/* If we have not seen this color before, find nearest colormap entry */
/* and update the cache */
if (*cachep == 0)
fill_inverse_cmap(cinfo, c0,c1,c2);
/* Now emit the colormap index for this cell */
*outptr++ = (JSAMPLE) (*cachep - 1);
}
}
}
METHODDEF(void)
pass2_fs_dither (j_decompress_ptr cinfo,
JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
/* This version performs Floyd-Steinberg dithering */
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
hist3d histogram = cquantize->histogram;
register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
register FSERRPTR errorptr; /* => fserrors[] at column before current */
JSAMPROW inptr; /* => current input pixel */
JSAMPROW outptr; /* => current output pixel */
histptr cachep;
int dir; /* +1 or -1 depending on direction */
int dir3; /* 3*dir, for advancing inptr & errorptr */
int row;
JDIMENSION col;
JDIMENSION width = cinfo->output_width;
JSAMPLE *range_limit = cinfo->sample_range_limit;
int *error_limit = cquantize->error_limiter;
JSAMPROW colormap0 = cinfo->colormap[0];
JSAMPROW colormap1 = cinfo->colormap[1];
JSAMPROW colormap2 = cinfo->colormap[2];
SHIFT_TEMPS
for (row = 0; row < num_rows; row++) {
inptr = input_buf[row];
outptr = output_buf[row];
if (cquantize->on_odd_row) {
/* work right to left in this row */
inptr += (width-1) * 3; /* so point to rightmost pixel */
outptr += width-1;
dir = -1;
dir3 = -3;
errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
cquantize->on_odd_row = FALSE; /* flip for next time */
} else {
/* work left to right in this row */
dir = 1;
dir3 = 3;
errorptr = cquantize->fserrors; /* => entry before first real column */
cquantize->on_odd_row = TRUE; /* flip for next time */
}
/* Preset error values: no error propagated to first pixel from left */
cur0 = cur1 = cur2 = 0;
/* and no error propagated to row below yet */
belowerr0 = belowerr1 = belowerr2 = 0;
bpreverr0 = bpreverr1 = bpreverr2 = 0;
for (col = width; col > 0; col--) {
/* curN holds the error propagated from the previous pixel on the
* current line. Add the error propagated from the previous line
* to form the complete error correction term for this pixel, and
* round the error term (which is expressed * 16) to an integer.
* RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
* for either sign of the error value.
* Note: errorptr points to *previous* column's array entry.
*/
cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
/* Limit the error using transfer function set by init_error_limit.
* See comments with init_error_limit for rationale.
*/
cur0 = error_limit[cur0];
cur1 = error_limit[cur1];
cur2 = error_limit[cur2];
/* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
* The maximum error is +- MAXJSAMPLE (or less with error limiting);
* this sets the required size of the range_limit array.
*/
cur0 += GETJSAMPLE(inptr[0]);
cur1 += GETJSAMPLE(inptr[1]);
cur2 += GETJSAMPLE(inptr[2]);
cur0 = GETJSAMPLE(range_limit[cur0]);
cur1 = GETJSAMPLE(range_limit[cur1]);
cur2 = GETJSAMPLE(range_limit[cur2]);
/* Index into the cache with adjusted pixel value */
cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
/* If we have not seen this color before, find nearest colormap */
/* entry and update the cache */
if (*cachep == 0)
fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
/* Now emit the colormap index for this cell */
{ register int pixcode = *cachep - 1;
*outptr = (JSAMPLE) pixcode;
/* Compute representation error for this pixel */
cur0 -= GETJSAMPLE(colormap0[pixcode]);
cur1 -= GETJSAMPLE(colormap1[pixcode]);
cur2 -= GETJSAMPLE(colormap2[pixcode]);
}
/* Compute error fractions to be propagated to adjacent pixels.
* Add these into the running sums, and simultaneously shift the
* next-line error sums left by 1 column.
*/
{ register LOCFSERROR bnexterr, delta;
bnexterr = cur0; /* Process component 0 */
delta = cur0 * 2;
cur0 += delta; /* form error * 3 */
errorptr[0] = (FSERROR) (bpreverr0 + cur0);
cur0 += delta; /* form error * 5 */
bpreverr0 = belowerr0 + cur0;
belowerr0 = bnexterr;
cur0 += delta; /* form error * 7 */
bnexterr = cur1; /* Process component 1 */
delta = cur1 * 2;
cur1 += delta; /* form error * 3 */
errorptr[1] = (FSERROR) (bpreverr1 + cur1);
cur1 += delta; /* form error * 5 */
bpreverr1 = belowerr1 + cur1;
belowerr1 = bnexterr;
cur1 += delta; /* form error * 7 */
bnexterr = cur2; /* Process component 2 */
delta = cur2 * 2;
cur2 += delta; /* form error * 3 */
errorptr[2] = (FSERROR) (bpreverr2 + cur2);
cur2 += delta; /* form error * 5 */
bpreverr2 = belowerr2 + cur2;
belowerr2 = bnexterr;
cur2 += delta; /* form error * 7 */
}
/* At this point curN contains the 7/16 error value to be propagated
* to the next pixel on the current line, and all the errors for the
* next line have been shifted over. We are therefore ready to move on.
*/
inptr += dir3; /* Advance pixel pointers to next column */
outptr += dir;
errorptr += dir3; /* advance errorptr to current column */
}
/* Post-loop cleanup: we must unload the final error values into the
* final fserrors[] entry. Note we need not unload belowerrN because
* it is for the dummy column before or after the actual array.
*/
errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
errorptr[1] = (FSERROR) bpreverr1;
errorptr[2] = (FSERROR) bpreverr2;
}
}
/*
* Initialize the error-limiting transfer function (lookup table).
* The raw F-S error computation can potentially compute error values of up to
* +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
* much less, otherwise obviously wrong pixels will be created. (Typical
* effects include weird fringes at color-area boundaries, isolated bright
* pixels in a dark area, etc.) The standard advice for avoiding this problem
* is to ensure that the "corners" of the color cube are allocated as output
* colors; then repeated errors in the same direction cannot cause cascading
* error buildup. However, that only prevents the error from getting
* completely out of hand; Aaron Giles reports that error limiting improves
* the results even with corner colors allocated.
* A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
* well, but the smoother transfer function used below is even better. Thanks
* to Aaron Giles for this idea.
*/
LOCAL(void)
init_error_limit (j_decompress_ptr cinfo)
/* Allocate and fill in the error_limiter table */
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
int * table;
int in, out;
table = (int *) (*cinfo->mem->alloc_small)
((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
cquantize->error_limiter = table;
#define STEPSIZE ((MAXJSAMPLE+1)/16)
/* Map errors 1:1 up to +- MAXJSAMPLE/16 */
out = 0;
for (in = 0; in < STEPSIZE; in++, out++) {
table[in] = out; table[-in] = -out;
}
/* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
table[in] = out; table[-in] = -out;
}
/* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
for (; in <= MAXJSAMPLE; in++) {
table[in] = out; table[-in] = -out;
}
#undef STEPSIZE
}
/*
* Finish up at the end of each pass.
*/
METHODDEF(void)
finish_pass1 (j_decompress_ptr cinfo)
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
/* Select the representative colors and fill in cinfo->colormap */
cinfo->colormap = cquantize->sv_colormap;
select_colors(cinfo, cquantize->desired);
/* Force next pass to zero the color index table */
cquantize->needs_zeroed = TRUE;
}
METHODDEF(void)
finish_pass2 (j_decompress_ptr cinfo)
{
/* no work */
}
/*
* Initialize for each processing pass.
*/
METHODDEF(void)
start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
hist3d histogram = cquantize->histogram;
int i;
/* Only F-S dithering or no dithering is supported. */
/* If user asks for ordered dither, give him F-S. */
if (cinfo->dither_mode != JDITHER_NONE)
cinfo->dither_mode = JDITHER_FS;
if (is_pre_scan) {
/* Set up method pointers */
cquantize->pub.color_quantize = prescan_quantize;
cquantize->pub.finish_pass = finish_pass1;
cquantize->needs_zeroed = TRUE; /* Always zero histogram */
} else {
/* Set up method pointers */
if (cinfo->dither_mode == JDITHER_FS)
cquantize->pub.color_quantize = pass2_fs_dither;
else
cquantize->pub.color_quantize = pass2_no_dither;
cquantize->pub.finish_pass = finish_pass2;
/* Make sure color count is acceptable */
i = cinfo->actual_number_of_colors;
if (i < 1)
ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
if (i > MAXNUMCOLORS)
ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
if (cinfo->dither_mode == JDITHER_FS) {
size_t arraysize = (size_t) ((cinfo->output_width + 2) *
(3 * SIZEOF(FSERROR)));
/* Allocate Floyd-Steinberg workspace if we didn't already. */
if (cquantize->fserrors == NULL)
cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
/* Initialize the propagated errors to zero. */
jzero_far((void FAR *) cquantize->fserrors, arraysize);
/* Make the error-limit table if we didn't already. */
if (cquantize->error_limiter == NULL)
init_error_limit(cinfo);
cquantize->on_odd_row = FALSE;
}
}
/* Zero the histogram or inverse color map, if necessary */
if (cquantize->needs_zeroed) {
for (i = 0; i < HIST_C0_ELEMS; i++) {
jzero_far((void FAR *) histogram[i],
HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
}
cquantize->needs_zeroed = FALSE;
}
}
/*
* Switch to a new external colormap between output passes.
*/
METHODDEF(void)
new_color_map_2_quant (j_decompress_ptr cinfo)
{
my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
/* Reset the inverse color map */
cquantize->needs_zeroed = TRUE;
}
/*
* Module initialization routine for 2-pass color quantization.
*/
GLOBAL(void)
jinit_2pass_quantizer (j_decompress_ptr cinfo)
{
my_cquantize_ptr cquantize;
int i;
cquantize = (my_cquantize_ptr)
(*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
SIZEOF(my_cquantizer));
cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
cquantize->pub.start_pass = start_pass_2_quant;
cquantize->pub.new_color_map = new_color_map_2_quant;
cquantize->fserrors = NULL; /* flag optional arrays not allocated */
cquantize->error_limiter = NULL;
/* Make sure jdmaster didn't give me a case I can't handle */
if (cinfo->out_color_components != 3)
ERREXIT(cinfo, JERR_NOTIMPL);
/* Allocate the histogram/inverse colormap storage */
cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
for (i = 0; i < HIST_C0_ELEMS; i++) {
cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
((j_common_ptr) cinfo, JPOOL_IMAGE,
HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
}
cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
/* Allocate storage for the completed colormap, if required.
* We do this now since it is FAR storage and may affect
* the memory manager's space calculations.
*/
if (cinfo->enable_2pass_quant) {
/* Make sure color count is acceptable */
int desired = cinfo->desired_number_of_colors;
/* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
if (desired < 8)
ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
/* Make sure colormap indexes can be represented by JSAMPLEs */
if (desired > MAXNUMCOLORS)
ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
cquantize->desired = desired;
} else
cquantize->sv_colormap = NULL;
/* Only F-S dithering or no dithering is supported. */
/* If user asks for ordered dither, give him F-S. */
if (cinfo->dither_mode != JDITHER_NONE)
cinfo->dither_mode = JDITHER_FS;
/* Allocate Floyd-Steinberg workspace if necessary.
* This isn't really needed until pass 2, but again it is FAR storage.
* Although we will cope with a later change in dither_mode,
* we do not promise to honor max_memory_to_use if dither_mode changes.
*/
if (cinfo->dither_mode == JDITHER_FS) {
cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
((j_common_ptr) cinfo, JPOOL_IMAGE,
(size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
/* Might as well create the error-limiting table too. */
init_error_limit(cinfo);
}
}
#endif /* QUANT_2PASS_SUPPORTED */