/*
* jquant2.c
*
* Copyright (C) 1991, 1992, Thomas G. Lane.
* This file is part of the Independent JPEG Group's software.
* For conditions of distribution and use, see the accompanying README file.
*
* This file contains 2-pass color quantization (color mapping) routines.
* These routines are invoked via the methods color_quant_prescan,
* color_quant_doit, and color_quant_init/term.
*/
#include "jinclude.h"
#ifdef QUANT_2PASS_SUPPORTED
/*
* This module implements the well-known Heckbert paradigm for color
* quantization. Most of the ideas used here can be traced back to
* Heckbert's seminal paper
* Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
* Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
*
* In the first pass over the image, we accumulate a histogram showing the
* usage count of each possible color. (To keep the histogram to a reasonable
* size, we reduce the precision of the input; typical practice is to retain
* 5 or 6 bits per color, so that 8 or 4 different input values are counted
* in the same histogram cell.) Next, the color-selection step begins with a
* box representing the whole color space, and repeatedly splits the "largest"
* remaining box until we have as many boxes as desired colors. Then the mean
* color in each remaining box becomes one of the possible output colors.
* The second pass over the image maps each input pixel to the closest output
* color (optionally after applying a Floyd-Steinberg dithering correction).
* This mapping is logically trivial, but making it go fast enough requires
* considerable care.
*
* Heckbert-style quantizers vary a good deal in their policies for choosing
* the "largest" box and deciding where to cut it. The particular policies
* used here have proved out well in experimental comparisons, but better ones
* may yet be found.
*
* The most significant difference between this quantizer and others is that
* this one is intended to operate in YCbCr colorspace, rather than RGB space
* as is usually done. Actually we work in scaled YCbCr colorspace, where
* Y distances are inflated by a factor of 2 relative to Cb or Cr distances.
* The empirical evidence is that distances in this space correspond to
* perceptual color differences more closely than do distances in RGB space;
* and working in this space is inexpensive within a JPEG decompressor, since
* the input data is already in YCbCr form. (We could transform to an even
* more perceptually linear space such as Lab or Luv, but that is very slow
* and doesn't yield much better results than scaled YCbCr.)
*/
#define Y_SCALE 2 /* scale Y distances up by this much */
#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
/*
* First we have the histogram data structure and routines for creating it.
*
* For work in YCbCr space, it is useful to keep more precision for Y than
* for Cb or Cr. We recommend keeping 6 bits for Y and 5 bits each for Cb/Cr.
* If you have plenty of memory and cycles, 6 bits all around gives marginally
* better results; if you are short of memory, 5 bits all around will save
* some space but degrade the results.
* To maintain a fully accurate histogram, we'd need to allocate a "long"
* (preferably unsigned long) for each cell. In practice this is overkill;
* we can get by with 16 bits per cell. Few of the cell counts will overflow,
* and clamping those that do overflow to the maximum value will give close-
* enough results. This reduces the recommended histogram size from 256Kb
* to 128Kb, which is a useful savings on PC-class machines.
* (In the second pass the histogram space is re-used for pixel mapping data;
* in that capacity, each cell must be able to store zero to the number of
* desired colors. 16 bits/cell is plenty for that too.)
* Since the JPEG code is intended to run in small memory model on 80x86
* machines, we can't just allocate the histogram in one chunk. Instead
* of a true 3-D array, we use a row of pointers to 2-D arrays. Each
* pointer corresponds to a Y value (typically 2^6 = 64 pointers) and
* each 2-D array has 2^5^2 = 1024 or 2^6^2 = 4096 entries. Note that
* on 80x86 machines, the pointer row is in near memory but the actual
* arrays are in far memory (same arrangement as we use for image arrays).
*/
#ifndef HIST_Y_BITS /* so you can override from Makefile */
#define HIST_Y_BITS 6 /* bits of precision in Y histogram */
#endif
#ifndef HIST_C_BITS /* so you can override from Makefile */
#define HIST_C_BITS 5 /* bits of precision in Cb/Cr histogram */
#endif
#define HIST_Y_ELEMS (1<image_width;
for (row = 0; row < num_rows; row++) {
ptr0 = image_data[0][row];
ptr1 = image_data[1][row];
ptr2 = image_data[2][row];
for (col = width; col > 0; col--) {
/* get pixel value and index into the histogram */
c0 = GETJSAMPLE(*ptr0++) >> Y_SHIFT;
c1 = GETJSAMPLE(*ptr1++) >> C_SHIFT;
c2 = GETJSAMPLE(*ptr2++) >> C_SHIFT;
histp = & histogram[c0][c1][c2];
/* increment, check for overflow and undo increment if so. */
/* We assume unsigned representation here! */
if (++(*histp) == 0)
(*histp)--;
}
}
}
/*
* Now we have the really interesting routines: selection of a colormap
* given the completed histogram.
* These routines work with a list of "boxes", each representing a rectangular
* subset of the input color space (to histogram precision).
*/
typedef struct {
/* The bounds of the box (inclusive); expressed as histogram indexes */
int c0min, c0max;
int c1min, c1max;
int c2min, c2max;
/* The number of nonzero histogram cells within this box */
long colorcount;
} box;
typedef box * boxptr;
static boxptr boxlist; /* array with room for desired # of boxes */
static int numboxes; /* number of boxes currently in boxlist */
static JSAMPARRAY my_colormap; /* the finished colormap (in YCbCr space) */
LOCAL boxptr
find_biggest_color_pop (void)
/* Find the splittable box with the largest color population */
/* Returns NULL if no splittable boxes remain */
{
register boxptr boxp;
register int i;
register long max = 0;
boxptr which = NULL;
for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
if (boxp->colorcount > max) {
if (boxp->c0max > boxp->c0min || boxp->c1max > boxp->c1min ||
boxp->c2max > boxp->c2min) {
which = boxp;
max = boxp->colorcount;
}
}
}
return which;
}
LOCAL boxptr
find_biggest_volume (void)
/* Find the splittable box with the largest (scaled) volume */
/* Returns NULL if no splittable boxes remain */
{
register boxptr boxp;
register int i;
register INT32 max = 0;
register INT32 norm, c0,c1,c2;
boxptr which = NULL;
/* We use 2-norm rather than real volume here.
* Some care is needed since the differences are expressed in
* histogram-cell units; if HIST_Y_BITS != HIST_C_BITS, we have to
* adjust the scaling to get the proper scaled-YCbCr-space distance.
* This code won't work right if HIST_Y_BITS < HIST_C_BITS,
* but that shouldn't ever be true.
* Note norm > 0 iff box is splittable, so need not check separately.
*/
for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
c0 = (boxp->c0max - boxp->c0min) * Y_SCALE;
c1 = (boxp->c1max - boxp->c1min) << (HIST_Y_BITS-HIST_C_BITS);
c2 = (boxp->c2max - boxp->c2min) << (HIST_Y_BITS-HIST_C_BITS);
norm = c0*c0 + c1*c1 + c2*c2;
if (norm > max) {
which = boxp;
max = norm;
}
}
return which;
}
LOCAL void
update_box (boxptr boxp)
/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
/* and recompute its population */
{
histptr histp;
int c0,c1,c2;
int c0min,c0max,c1min,c1max,c2min,c2max;
long ccount;
c0min = boxp->c0min; c0max = boxp->c0max;
c1min = boxp->c1min; c1max = boxp->c1max;
c2min = boxp->c2min; c2max = boxp->c2max;
if (c0max > c0min)
for (c0 = c0min; c0 <= c0max; c0++)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c0min = c0min = c0;
goto have_c0min;
}
}
have_c0min:
if (c0max > c0min)
for (c0 = c0max; c0 >= c0min; c0--)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c0max = c0max = c0;
goto have_c0max;
}
}
have_c0max:
if (c1max > c1min)
for (c1 = c1min; c1 <= c1max; c1++)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c1min = c1min = c1;
goto have_c1min;
}
}
have_c1min:
if (c1max > c1min)
for (c1 = c1max; c1 >= c1min; c1--)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++)
if (*histp++ != 0) {
boxp->c1max = c1max = c1;
goto have_c1max;
}
}
have_c1max:
if (c2max > c2min)
for (c2 = c2min; c2 <= c2max; c2++)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1min][c2];
for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C_ELEMS)
if (*histp != 0) {
boxp->c2min = c2min = c2;
goto have_c2min;
}
}
have_c2min:
if (c2max > c2min)
for (c2 = c2max; c2 >= c2min; c2--)
for (c0 = c0min; c0 <= c0max; c0++) {
histp = & histogram[c0][c1min][c2];
for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C_ELEMS)
if (*histp != 0) {
boxp->c2max = c2max = c2;
goto have_c2max;
}
}
have_c2max:
/* Now scan remaining volume of box and compute population */
ccount = 0;
for (c0 = c0min; c0 <= c0max; c0++)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++, histp++)
if (*histp != 0) {
ccount++;
}
}
boxp->colorcount = ccount;
}
LOCAL void
median_cut (int desired_colors)
/* Repeatedly select and split the largest box until we have enough boxes */
{
int n,lb;
int c0,c1,c2,cmax;
register boxptr b1,b2;
while (numboxes < desired_colors) {
/* Select box to split */
/* Current algorithm: by population for first half, then by volume */
if (numboxes*2 <= desired_colors) {
b1 = find_biggest_color_pop();
} else {
b1 = find_biggest_volume();
}
if (b1 == NULL) /* no splittable boxes left! */
break;
b2 = &boxlist[numboxes]; /* where new box will go */
/* Copy the color bounds to the new box. */
b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
/* Choose which axis to split the box on.
* Current algorithm: longest scaled axis.
* See notes in find_biggest_volume about scaling...
*/
c0 = (b1->c0max - b1->c0min) * Y_SCALE;
c1 = (b1->c1max - b1->c1min) << (HIST_Y_BITS-HIST_C_BITS);
c2 = (b1->c2max - b1->c2min) << (HIST_Y_BITS-HIST_C_BITS);
cmax = c0; n = 0;
if (c1 > cmax) { cmax = c1; n = 1; }
if (c2 > cmax) { n = 2; }
/* Choose split point along selected axis, and update box bounds.
* Current algorithm: split at halfway point.
* (Since the box has been shrunk to minimum volume,
* any split will produce two nonempty subboxes.)
* Note that lb value is max for lower box, so must be < old max.
*/
switch (n) {
case 0:
lb = (b1->c0max + b1->c0min) / 2;
b1->c0max = lb;
b2->c0min = lb+1;
break;
case 1:
lb = (b1->c1max + b1->c1min) / 2;
b1->c1max = lb;
b2->c1min = lb+1;
break;
case 2:
lb = (b1->c2max + b1->c2min) / 2;
b1->c2max = lb;
b2->c2min = lb+1;
break;
}
/* Update stats for boxes */
update_box(b1);
update_box(b2);
numboxes++;
}
}
LOCAL void
compute_color (boxptr boxp, int icolor)
/* Compute representative color for a box, put it in my_colormap[icolor] */
{
/* Current algorithm: mean weighted by pixels (not colors) */
/* Note it is important to get the rounding correct! */
histptr histp;
int c0,c1,c2;
int c0min,c0max,c1min,c1max,c2min,c2max;
long count;
long total = 0;
long c0total = 0;
long c1total = 0;
long c2total = 0;
c0min = boxp->c0min; c0max = boxp->c0max;
c1min = boxp->c1min; c1max = boxp->c1max;
c2min = boxp->c2min; c2max = boxp->c2max;
for (c0 = c0min; c0 <= c0max; c0++)
for (c1 = c1min; c1 <= c1max; c1++) {
histp = & histogram[c0][c1][c2min];
for (c2 = c2min; c2 <= c2max; c2++) {
if ((count = *histp++) != 0) {
total += count;
c0total += ((c0 << Y_SHIFT) + ((1<>1)) * count;
c1total += ((c1 << C_SHIFT) + ((1<>1)) * count;
c2total += ((c2 << C_SHIFT) + ((1<>1)) * count;
}
}
}
my_colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
my_colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
my_colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
}
LOCAL void
remap_colormap (decompress_info_ptr cinfo)
/* Remap the internal colormap to the output colorspace */
{
/* This requires a little trickery since color_convert expects to
* deal with 3-D arrays (a 2-D sample array for each component).
* We must promote the colormaps into one-row 3-D arrays.
*/
short ci;
JSAMPARRAY input_hack[3];
JSAMPARRAY output_hack[10]; /* assume no more than 10 output components */
for (ci = 0; ci < 3; ci++)
input_hack[ci] = &(my_colormap[ci]);
for (ci = 0; ci < cinfo->color_out_comps; ci++)
output_hack[ci] = &(cinfo->colormap[ci]);
(*cinfo->methods->color_convert) (cinfo, 1,
(long) cinfo->actual_number_of_colors,
input_hack, output_hack);
}
LOCAL void
select_colors (decompress_info_ptr cinfo)
/* Master routine for color selection */
{
int desired = cinfo->desired_number_of_colors;
int i;
/* Allocate workspace for box list */
boxlist = (boxptr) (*cinfo->emethods->alloc_small) (desired * SIZEOF(box));
/* Initialize one box containing whole space */
numboxes = 1;
boxlist[0].c0min = 0;
boxlist[0].c0max = MAXJSAMPLE >> Y_SHIFT;
boxlist[0].c1min = 0;
boxlist[0].c1max = MAXJSAMPLE >> C_SHIFT;
boxlist[0].c2min = 0;
boxlist[0].c2max = MAXJSAMPLE >> C_SHIFT;
/* Shrink it to actually-used volume and set its statistics */
update_box(& boxlist[0]);
/* Perform median-cut to produce final box list */
median_cut(desired);
/* Compute the representative color for each box, fill my_colormap[] */
for (i = 0; i < numboxes; i++)
compute_color(& boxlist[i], i);
cinfo->actual_number_of_colors = numboxes;
/* Produce an output colormap in the desired output colorspace */
remap_colormap(cinfo);
TRACEMS1(cinfo->emethods, 1, "Selected %d colors for quantization",
numboxes);
/* Done with the box list */
(*cinfo->emethods->free_small) ((void *) boxlist);
}
/*
* These routines are concerned with the time-critical task of mapping input
* colors to the nearest color in the selected colormap.
*
* We re-use the histogram space as an "inverse color map", essentially a
* cache for the results of nearest-color searches. All colors within a
* histogram cell will be mapped to the same colormap entry, namely the one
* closest to the cell's center. This may not be quite the closest entry to
* the actual input color, but it's almost as good. A zero in the cache
* indicates we haven't found the nearest color for that cell yet; the array
* is cleared to zeroes before starting the mapping pass. When we find the
* nearest color for a cell, its colormap index plus one is recorded in the
* cache for future use. The pass2 scanning routines call fill_inverse_cmap
* when they need to use an unfilled entry in the cache.
*
* Our method of efficiently finding nearest colors is based on the "locally
* sorted search" idea described by Heckbert and on the incremental distance
* calculation described by Spencer W. Thomas in chapter III.1 of Graphics
* Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
* the distances from a given colormap entry to each cell of the histogram can
* be computed quickly using an incremental method: the differences between
* distances to adjacent cells themselves differ by a constant. This allows a
* fairly fast implementation of the "brute force" approach of computing the
* distance from every colormap entry to every histogram cell. Unfortunately,
* it needs a work array to hold the best-distance-so-far for each histogram
* cell (because the inner loop has to be over cells, not colormap entries).
* The work array elements have to be INT32s, so the work array would need
* 256Kb at our recommended precision. This is not feasible in DOS machines.
* Another disadvantage of the brute force approach is that it computes
* distances to every cell of the cubical histogram. When working with YCbCr
* input, only about a quarter of the cube represents realizable colors, so
* many of the cells will never be used and filling them is wasted effort.
*
* To get around these problems, we apply Thomas' method to compute the
* nearest colors for only the cells within a small subbox of the histogram.
* The work array need be only as big as the subbox, so the memory usage
* problem is solved. A subbox is processed only when some cell in it is
* referenced by the pass2 routines, so we will never bother with cells far
* outside the realizable color volume. An additional advantage of this
* approach is that we can apply Heckbert's locality criterion to quickly
* eliminate colormap entries that are far away from the subbox; typically
* three-fourths of the colormap entries are rejected by Heckbert's criterion,
* and we need not compute their distances to individual cells in the subbox.
* The speed of this approach is heavily influenced by the subbox size: too
* small means too much overhead, too big loses because Heckbert's criterion
* can't eliminate as many colormap entries. Empirically the best subbox
* size seems to be about 1/512th of the histogram (1/8th in each direction).
*
* Thomas' article also describes a refined method which is asymptotically
* faster than the brute-force method, but it is also far more complex and
* cannot efficiently be applied to small subboxes. It is therefore not
* useful for programs intended to be portable to DOS machines. On machines
* with plenty of memory, filling the whole histogram in one shot with Thomas'
* refined method might be faster than the present code --- but then again,
* it might not be any faster, and it's certainly more complicated.
*/
#ifndef BOX_Y_LOG /* so you can override from Makefile */
#define BOX_Y_LOG (HIST_Y_BITS-3) /* log2(hist cells in update box, Y axis) */
#endif
#ifndef BOX_C_LOG /* so you can override from Makefile */
#define BOX_C_LOG (HIST_C_BITS-3) /* log2(hist cells in update box, C axes) */
#endif
#define BOX_Y_ELEMS (1<actual_number_of_colors;
int maxc0, maxc1, maxc2;
int centerc0, centerc1, centerc2;
int i, x, ncolors;
INT32 minmaxdist, min_dist, max_dist, tdist;
INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
/* Compute true coordinates of update box's upper corner and center.
* Actually we compute the coordinates of the center of the upper-corner
* histogram cell, which are the upper bounds of the volume we care about.
* Note that since ">>" rounds down, the "center" values may be closer to
* min than to max; hence comparisons to them must be "<=", not "<".
*/
maxc0 = minc0 + ((1 << BOX_Y_SHIFT) - (1 << Y_SHIFT));
centerc0 = (minc0 + maxc0) >> 1;
maxc1 = minc1 + ((1 << BOX_C_SHIFT) - (1 << C_SHIFT));
centerc1 = (minc1 + maxc1) >> 1;
maxc2 = minc2 + ((1 << BOX_C_SHIFT) - (1 << C_SHIFT));
centerc2 = (minc2 + maxc2) >> 1;
/* For each color in colormap, find:
* 1. its minimum squared-distance to any point in the update box
* (zero if color is within update box);
* 2. its maximum squared-distance to any point in the update box.
* Both of these can be found by considering only the corners of the box.
* We save the minimum distance for each color in mindist[];
* only the smallest maximum distance is of interest.
* Note we have to scale Y to get correct distance in scaled space.
*/
minmaxdist = 0x7FFFFFFFL;
for (i = 0; i < numcolors; i++) {
/* We compute the squared-c0-distance term, then add in the other two. */
x = GETJSAMPLE(my_colormap[0][i]);
if (x < minc0) {
tdist = (x - minc0) * Y_SCALE;
min_dist = tdist*tdist;
tdist = (x - maxc0) * Y_SCALE;
max_dist = tdist*tdist;
} else if (x > maxc0) {
tdist = (x - maxc0) * Y_SCALE;
min_dist = tdist*tdist;
tdist = (x - minc0) * Y_SCALE;
max_dist = tdist*tdist;
} else {
/* within cell range so no contribution to min_dist */
min_dist = 0;
if (x <= centerc0) {
tdist = (x - maxc0) * Y_SCALE;
max_dist = tdist*tdist;
} else {
tdist = (x - minc0) * Y_SCALE;
max_dist = tdist*tdist;
}
}
x = GETJSAMPLE(my_colormap[1][i]);
if (x < minc1) {
tdist = x - minc1;
min_dist += tdist*tdist;
tdist = x - maxc1;
max_dist += tdist*tdist;
} else if (x > maxc1) {
tdist = x - maxc1;
min_dist += tdist*tdist;
tdist = x - minc1;
max_dist += tdist*tdist;
} else {
/* within cell range so no contribution to min_dist */
if (x <= centerc1) {
tdist = x - maxc1;
max_dist += tdist*tdist;
} else {
tdist = x - minc1;
max_dist += tdist*tdist;
}
}
x = GETJSAMPLE(my_colormap[2][i]);
if (x < minc2) {
tdist = x - minc2;
min_dist += tdist*tdist;
tdist = x - maxc2;
max_dist += tdist*tdist;
} else if (x > maxc2) {
tdist = x - maxc2;
min_dist += tdist*tdist;
tdist = x - minc2;
max_dist += tdist*tdist;
} else {
/* within cell range so no contribution to min_dist */
if (x <= centerc2) {
tdist = x - maxc2;
max_dist += tdist*tdist;
} else {
tdist = x - minc2;
max_dist += tdist*tdist;
}
}
mindist[i] = min_dist; /* save away the results */
if (max_dist < minmaxdist)
minmaxdist = max_dist;
}
/* Now we know that no cell in the update box is more than minmaxdist
* away from some colormap entry. Therefore, only colors that are
* within minmaxdist of some part of the box need be considered.
*/
ncolors = 0;
for (i = 0; i < numcolors; i++) {
if (mindist[i] <= minmaxdist)
colorlist[ncolors++] = (JSAMPLE) i;
}
return ncolors;
}
LOCAL void
find_best_colors (decompress_info_ptr cinfo, int minc0, int minc1, int minc2,
int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
/* Find the closest colormap entry for each cell in the update box,
* given the list of candidate colors prepared by find_nearby_colors.
* Return the indexes of the closest entries in the bestcolor[] array.
* This routine uses Thomas' incremental distance calculation method to
* find the distance from a colormap entry to successive cells in the box.
*/
{
int ic0, ic1, ic2;
int i, icolor;
register INT32 * bptr; /* pointer into bestdist[] array */
JSAMPLE * cptr; /* pointer into bestcolor[] array */
INT32 dist0, dist1; /* initial distance values */
register INT32 dist2; /* current distance in inner loop */
INT32 xx0, xx1; /* distance increments */
register INT32 xx2;
INT32 inc0, inc1, inc2; /* initial values for increments */
/* This array holds the distance to the nearest-so-far color for each cell */
INT32 bestdist[BOX_Y_ELEMS * BOX_C_ELEMS * BOX_C_ELEMS];
/* Initialize best-distance for each cell of the update box */
bptr = bestdist;
for (i = BOX_Y_ELEMS*BOX_C_ELEMS*BOX_C_ELEMS-1; i >= 0; i--)
*bptr++ = 0x7FFFFFFFL;
/* For each color selected by find_nearby_colors,
* compute its distance to the center of each cell in the box.
* If that's less than best-so-far, update best distance and color number.
* Note we have to scale Y to get correct distance in scaled space.
*/
/* Nominal steps between cell centers ("x" in Thomas article) */
#define STEP_Y ((1 << Y_SHIFT) * Y_SCALE)
#define STEP_C (1 << C_SHIFT)
for (i = 0; i < numcolors; i++) {
icolor = GETJSAMPLE(colorlist[i]);
/* Compute (square of) distance from minc0/c1/c2 to this color */
inc0 = (minc0 - (int) GETJSAMPLE(my_colormap[0][icolor])) * Y_SCALE;
dist0 = inc0*inc0;
inc1 = minc1 - (int) GETJSAMPLE(my_colormap[1][icolor]);
dist0 += inc1*inc1;
inc2 = minc2 - (int) GETJSAMPLE(my_colormap[2][icolor]);
dist0 += inc2*inc2;
/* Form the initial difference increments */
inc0 = inc0 * (2 * STEP_Y) + STEP_Y * STEP_Y;
inc1 = inc1 * (2 * STEP_C) + STEP_C * STEP_C;
inc2 = inc2 * (2 * STEP_C) + STEP_C * STEP_C;
/* Now loop over all cells in box, updating distance per Thomas method */
bptr = bestdist;
cptr = bestcolor;
xx0 = inc0;
for (ic0 = BOX_Y_ELEMS-1; ic0 >= 0; ic0--) {
dist1 = dist0;
xx1 = inc1;
for (ic1 = BOX_C_ELEMS-1; ic1 >= 0; ic1--) {
dist2 = dist1;
xx2 = inc2;
for (ic2 = BOX_C_ELEMS-1; ic2 >= 0; ic2--) {
if (dist2 < *bptr) {
*bptr = dist2;
*cptr = (JSAMPLE) icolor;
}
dist2 += xx2;
xx2 += 2 * STEP_C * STEP_C;
bptr++;
cptr++;
}
dist1 += xx1;
xx1 += 2 * STEP_C * STEP_C;
}
dist0 += xx0;
xx0 += 2 * STEP_Y * STEP_Y;
}
}
}
LOCAL void
fill_inverse_cmap (decompress_info_ptr cinfo, int c0, int c1, int c2)
/* Fill the inverse-colormap entries in the update box that contains */
/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
/* we can fill as many others as we wish.) */
{
int minc0, minc1, minc2; /* lower left corner of update box */
int ic0, ic1, ic2;
register JSAMPLE * cptr; /* pointer into bestcolor[] array */
register histptr cachep; /* pointer into main cache array */
/* This array lists the candidate colormap indexes. */
JSAMPLE colorlist[MAXNUMCOLORS];
int numcolors; /* number of candidate colors */
/* This array holds the actually closest colormap index for each cell. */
JSAMPLE bestcolor[BOX_Y_ELEMS * BOX_C_ELEMS * BOX_C_ELEMS];
/* Convert cell coordinates to update box ID */
c0 >>= BOX_Y_LOG;
c1 >>= BOX_C_LOG;
c2 >>= BOX_C_LOG;
/* Compute true coordinates of update box's origin corner.
* Actually we compute the coordinates of the center of the corner
* histogram cell, which are the lower bounds of the volume we care about.
*/
minc0 = (c0 << BOX_Y_SHIFT) + ((1 << Y_SHIFT) >> 1);
minc1 = (c1 << BOX_C_SHIFT) + ((1 << C_SHIFT) >> 1);
minc2 = (c2 << BOX_C_SHIFT) + ((1 << C_SHIFT) >> 1);
/* Determine which colormap entries are close enough to be candidates
* for the nearest entry to some cell in the update box.
*/
numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
/* Determine the actually nearest colors. */
find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
bestcolor);
/* Save the best color numbers (plus 1) in the main cache array */
c0 <<= BOX_Y_LOG; /* convert ID back to base cell indexes */
c1 <<= BOX_C_LOG;
c2 <<= BOX_C_LOG;
cptr = bestcolor;
for (ic0 = 0; ic0 < BOX_Y_ELEMS; ic0++) {
for (ic1 = 0; ic1 < BOX_C_ELEMS; ic1++) {
cachep = & histogram[c0+ic0][c1+ic1][c2];
for (ic2 = 0; ic2 < BOX_C_ELEMS; ic2++) {
*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
}
}
}
}
/*
* These routines perform second-pass scanning of the image: map each pixel to
* the proper colormap index, and output the indexes to the output file.
*
* output_workspace is a one-component array of pixel dimensions at least
* as large as the input image strip; it can be used to hold the converted
* pixels' colormap indexes.
*/
METHODDEF void
pass2_nodither (decompress_info_ptr cinfo, int num_rows,
JSAMPIMAGE image_data, JSAMPARRAY output_workspace)
/* This version performs no dithering */
{
register JSAMPROW ptr0, ptr1, ptr2, outptr;
register histptr cachep;
register int c0, c1, c2;
int row;
long col;
long width = cinfo->image_width;
/* Convert data to colormap indexes, which we save in output_workspace */
for (row = 0; row < num_rows; row++) {
ptr0 = image_data[0][row];
ptr1 = image_data[1][row];
ptr2 = image_data[2][row];
outptr = output_workspace[row];
for (col = width; col > 0; col--) {
/* get pixel value and index into the cache */
c0 = GETJSAMPLE(*ptr0++) >> Y_SHIFT;
c1 = GETJSAMPLE(*ptr1++) >> C_SHIFT;
c2 = GETJSAMPLE(*ptr2++) >> C_SHIFT;
cachep = & histogram[c0][c1][c2];
/* If we have not seen this color before, find nearest colormap entry */
/* and update the cache */
if (*cachep == 0)
fill_inverse_cmap(cinfo, c0,c1,c2);
/* Now emit the colormap index for this cell */
*outptr++ = (JSAMPLE) (*cachep - 1);
}
}
/* Emit converted rows to the output file */
(*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &output_workspace);
}
/* Declarations for Floyd-Steinberg dithering.
*
* Errors are accumulated into the arrays evenrowerrs[] and oddrowerrs[].
* These have resolutions of 1/16th of a pixel count. The error at a given
* pixel is propagated to its unprocessed neighbors using the standard F-S
* fractions,
* ... (here) 7/16
* 3/16 5/16 1/16
* We work left-to-right on even rows, right-to-left on odd rows.
*
* Each of the arrays has (#columns + 2) entries; the extra entry
* at each end saves us from special-casing the first and last pixels.
* Each entry is three values long.
* In evenrowerrs[], the entries for a component are stored left-to-right, but
* in oddrowerrs[] they are stored right-to-left. This means we always
* process the current row's error entries in increasing order and the next
* row's error entries in decreasing order, regardless of whether we are
* working L-to-R or R-to-L in the pixel data!
*
* Note: on a wide image, we might not have enough room in a PC's near data
* segment to hold the error arrays; so they are allocated with alloc_medium.
*/
#ifdef EIGHT_BIT_SAMPLES
typedef INT16 FSERROR; /* 16 bits should be enough */
#else
typedef INT32 FSERROR; /* may need more than 16 bits? */
#endif
typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */
static FSERRPTR evenrowerrs, oddrowerrs; /* current-row and next-row errors */
static boolean on_odd_row; /* flag to remember which row we are on */
METHODDEF void
pass2_dither (decompress_info_ptr cinfo, int num_rows,
JSAMPIMAGE image_data, JSAMPARRAY output_workspace)
/* This version performs Floyd-Steinberg dithering */
{
register FSERROR val;
register FSERRPTR thisrowerr, nextrowerr;
register FSERROR c0, c1, c2;
register int pixcode;
JSAMPROW ptr0, ptr1, ptr2, outptr;
histptr cachep;
int dir;
long col;
int row;
long width = cinfo->image_width;
/* Convert data to colormap indexes, which we save in output_workspace */
for (row = 0; row < num_rows; row++) {
ptr0 = image_data[0][row];
ptr1 = image_data[1][row];
ptr2 = image_data[2][row];
outptr = output_workspace[row];
if (on_odd_row) {
/* work right to left in this row */
ptr0 += width - 1;
ptr1 += width - 1;
ptr2 += width - 1;
outptr += width - 1;
dir = -1;
thisrowerr = oddrowerrs + 3;
nextrowerr = evenrowerrs + width*3;
on_odd_row = FALSE; /* flip for next time */
} else {
/* work left to right in this row */
dir = 1;
thisrowerr = evenrowerrs + 3;
nextrowerr = oddrowerrs + width*3;
on_odd_row = TRUE; /* flip for next time */
}
/* need only initialize this one entry in nextrowerr */
nextrowerr[0] = nextrowerr[1] = nextrowerr[2] = 0;
for (col = width; col > 0; col--) {
/* Get this pixel's value and add accumulated errors */
/* The errors are in units of 1/16th pixel value */
val = (GETJSAMPLE(*ptr0) << 4) + thisrowerr[0];
if (val <= 0) val = 0; /* must watch for range overflow! */
else {
val += 8; /* divide by 16 with proper rounding */
val >>= 4;
if (val > MAXJSAMPLE) val = MAXJSAMPLE;
}
c0 = val;
val = (GETJSAMPLE(*ptr1) << 4) + thisrowerr[1];
if (val <= 0) val = 0; /* must watch for range overflow! */
else {
val += 8; /* divide by 16 with proper rounding */
val >>= 4;
if (val > MAXJSAMPLE) val = MAXJSAMPLE;
}
c1 = val;
val = (GETJSAMPLE(*ptr2) << 4) + thisrowerr[2];
if (val <= 0) val = 0; /* must watch for range overflow! */
else {
val += 8; /* divide by 16 with proper rounding */
val >>= 4;
if (val > MAXJSAMPLE) val = MAXJSAMPLE;
}
c2 = val;
/* Index into the cache with adjusted value */
cachep = & histogram[c0 >> Y_SHIFT][c1 >> C_SHIFT][c2 >> C_SHIFT];
/* If we have not seen this color before, find nearest colormap */
/* entry and update the cache */
if (*cachep == 0)
fill_inverse_cmap(cinfo, c0 >> Y_SHIFT, c1 >> C_SHIFT, c2 >> C_SHIFT);
/* Now emit the colormap index for this cell */
pixcode = *cachep - 1;
*outptr = (JSAMPLE) pixcode;
/* Compute representation error for this pixel */
c0 -= (FSERROR) GETJSAMPLE(my_colormap[0][pixcode]);
c1 -= (FSERROR) GETJSAMPLE(my_colormap[1][pixcode]);
c2 -= (FSERROR) GETJSAMPLE(my_colormap[2][pixcode]);
/* Propagate error to adjacent pixels */
/* Remember that nextrowerr entries are in reverse order! */
val = c0 * 2;
nextrowerr[0-3] = c0; /* not +=, since not initialized yet */
c0 += val; /* form error * 3 */
nextrowerr[0+3] += c0;
c0 += val; /* form error * 5 */
nextrowerr[0 ] += c0;
c0 += val; /* form error * 7 */
thisrowerr[0+3] += c0;
val = c1 * 2;
nextrowerr[1-3] = c1; /* not +=, since not initialized yet */
c1 += val; /* form error * 3 */
nextrowerr[1+3] += c1;
c1 += val; /* form error * 5 */
nextrowerr[1 ] += c1;
c1 += val; /* form error * 7 */
thisrowerr[1+3] += c1;
val = c2 * 2;
nextrowerr[2-3] = c2; /* not +=, since not initialized yet */
c2 += val; /* form error * 3 */
nextrowerr[2+3] += c2;
c2 += val; /* form error * 5 */
nextrowerr[2 ] += c2;
c2 += val; /* form error * 7 */
thisrowerr[2+3] += c2;
/* Advance to next column */
ptr0 += dir;
ptr1 += dir;
ptr2 += dir;
outptr += dir;
thisrowerr += 3; /* cur-row error ptr advances to right */
nextrowerr -= 3; /* next-row error ptr advances to left */
}
}
/* Emit converted rows to the output file */
(*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &output_workspace);
}
/*
* Initialize for two-pass color quantization.
*/
METHODDEF void
color_quant_init (decompress_info_ptr cinfo)
{
int i;
/* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
if (cinfo->desired_number_of_colors < 8)
ERREXIT(cinfo->emethods, "Cannot request less than 8 quantized colors");
/* Make sure colormap indexes can be represented by JSAMPLEs */
if (cinfo->desired_number_of_colors > MAXNUMCOLORS)
ERREXIT1(cinfo->emethods, "Cannot request more than %d quantized colors",
MAXNUMCOLORS);
/* Allocate and zero the histogram */
histogram = (hist3d) (*cinfo->emethods->alloc_small)
(HIST_Y_ELEMS * SIZEOF(hist2d));
for (i = 0; i < HIST_Y_ELEMS; i++) {
histogram[i] = (hist2d) (*cinfo->emethods->alloc_medium)
(HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell));
jzero_far((void FAR *) histogram[i],
HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell));
}
/* Allocate storage for the internal and external colormaps. */
/* We do this now since it is FAR storage and may affect the memory */
/* manager's space calculations. */
my_colormap = (*cinfo->emethods->alloc_small_sarray)
((long) cinfo->desired_number_of_colors,
(long) 3);
cinfo->colormap = (*cinfo->emethods->alloc_small_sarray)
((long) cinfo->desired_number_of_colors,
(long) cinfo->color_out_comps);
/* Allocate Floyd-Steinberg workspace if necessary */
/* This isn't needed until pass 2, but again it is FAR storage. */
if (cinfo->use_dithering) {
size_t arraysize = (size_t) ((cinfo->image_width + 2L) * 3L * SIZEOF(FSERROR));
evenrowerrs = (FSERRPTR) (*cinfo->emethods->alloc_medium) (arraysize);
oddrowerrs = (FSERRPTR) (*cinfo->emethods->alloc_medium) (arraysize);
/* we only need to zero the forward contribution for current row. */
jzero_far((void FAR *) evenrowerrs, arraysize);
on_odd_row = FALSE;
}
/* Indicate number of passes needed, excluding the prescan pass. */
cinfo->total_passes++; /* I always use one pass */
}
/*
* Perform two-pass quantization: rescan the image data and output the
* converted data via put_color_map and put_pixel_rows.
* The source_method is a routine that can scan the image data; it can
* be called as many times as desired. The processing routine called by
* source_method has the same interface as color_quantize does in the
* one-pass case, except it must call put_pixel_rows itself. (This allows
* me to use multiple passes in which earlier passes don't output anything.)
*/
METHODDEF void
color_quant_doit (decompress_info_ptr cinfo, quantize_caller_ptr source_method)
{
int i;
/* Select the representative colors */
select_colors(cinfo);
/* Pass the external colormap to the output module. */
/* NB: the output module may continue to use the colormap until shutdown. */
(*cinfo->methods->put_color_map) (cinfo, cinfo->actual_number_of_colors,
cinfo->colormap);
/* Re-zero the histogram so pass 2 can use it as nearest-color cache */
for (i = 0; i < HIST_Y_ELEMS; i++) {
jzero_far((void FAR *) histogram[i],
HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell));
}
/* Perform pass 2 */
if (cinfo->use_dithering)
(*source_method) (cinfo, pass2_dither);
else
(*source_method) (cinfo, pass2_nodither);
}
/*
* Finish up at the end of the file.
*/
METHODDEF void
color_quant_term (decompress_info_ptr cinfo)
{
/* no work (we let free_all release the histogram/cache and colormaps) */
/* Note that we *mustn't* free the external colormap before free_all, */
/* since output module may use it! */
}
/*
* Map some rows of pixels to the output colormapped representation.
* Not used in two-pass case.
*/
METHODDEF void
color_quantize (decompress_info_ptr cinfo, int num_rows,
JSAMPIMAGE input_data, JSAMPARRAY output_data)
{
ERREXIT(cinfo->emethods, "Should not get here!");
}
/*
* The method selection routine for 2-pass color quantization.
*/
GLOBAL void
jsel2quantize (decompress_info_ptr cinfo)
{
if (cinfo->two_pass_quantize) {
/* Make sure jdmaster didn't give me a case I can't handle */
if (cinfo->num_components != 3 || cinfo->jpeg_color_space != CS_YCbCr)
ERREXIT(cinfo->emethods, "2-pass quantization only handles YCbCr input");
cinfo->methods->color_quant_init = color_quant_init;
cinfo->methods->color_quant_prescan = color_quant_prescan;
cinfo->methods->color_quant_doit = color_quant_doit;
cinfo->methods->color_quant_term = color_quant_term;
cinfo->methods->color_quantize = color_quantize;
}
}
#endif /* QUANT_2PASS_SUPPORTED */