1/*
2 * jquant2.c
3 *
4 * This file was part of the Independent JPEG Group's software:
5 * Copyright (C) 1991-1996, Thomas G. Lane.
6 * libjpeg-turbo Modifications:
7 * Copyright (C) 2009, 2014-2015, D. R. Commander.
8 * For conditions of distribution and use, see the accompanying README.ijg
9 * file.
10 *
11 * This file contains 2-pass color quantization (color mapping) routines.
12 * These routines provide selection of a custom color map for an image,
13 * followed by mapping of the image to that color map, with optional
14 * Floyd-Steinberg dithering.
15 * It is also possible to use just the second pass to map to an arbitrary
16 * externally-given color map.
17 *
18 * Note: ordered dithering is not supported, since there isn't any fast
19 * way to compute intercolor distances; it's unclear that ordered dither's
20 * fundamental assumptions even hold with an irregularly spaced color map.
21 */
22
23#define JPEG_INTERNALS
24#include "jinclude.h"
25#include "jpeglib.h"
26
27#ifdef QUANT_2PASS_SUPPORTED
28
29
30/*
31 * This module implements the well-known Heckbert paradigm for color
32 * quantization. Most of the ideas used here can be traced back to
33 * Heckbert's seminal paper
34 * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
35 * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
36 *
37 * In the first pass over the image, we accumulate a histogram showing the
38 * usage count of each possible color. To keep the histogram to a reasonable
39 * size, we reduce the precision of the input; typical practice is to retain
40 * 5 or 6 bits per color, so that 8 or 4 different input values are counted
41 * in the same histogram cell.
42 *
43 * Next, the color-selection step begins with a box representing the whole
44 * color space, and repeatedly splits the "largest" remaining box until we
45 * have as many boxes as desired colors. Then the mean color in each
46 * remaining box becomes one of the possible output colors.
47 *
48 * The second pass over the image maps each input pixel to the closest output
49 * color (optionally after applying a Floyd-Steinberg dithering correction).
50 * This mapping is logically trivial, but making it go fast enough requires
51 * considerable care.
52 *
53 * Heckbert-style quantizers vary a good deal in their policies for choosing
54 * the "largest" box and deciding where to cut it. The particular policies
55 * used here have proved out well in experimental comparisons, but better ones
56 * may yet be found.
57 *
58 * In earlier versions of the IJG code, this module quantized in YCbCr color
59 * space, processing the raw upsampled data without a color conversion step.
60 * This allowed the color conversion math to be done only once per colormap
61 * entry, not once per pixel. However, that optimization precluded other
62 * useful optimizations (such as merging color conversion with upsampling)
63 * and it also interfered with desired capabilities such as quantizing to an
64 * externally-supplied colormap. We have therefore abandoned that approach.
65 * The present code works in the post-conversion color space, typically RGB.
66 *
67 * To improve the visual quality of the results, we actually work in scaled
68 * RGB space, giving G distances more weight than R, and R in turn more than
69 * B. To do everything in integer math, we must use integer scale factors.
70 * The 2/3/1 scale factors used here correspond loosely to the relative
71 * weights of the colors in the NTSC grayscale equation.
72 * If you want to use this code to quantize a non-RGB color space, you'll
73 * probably need to change these scale factors.
74 */
75
76#define R_SCALE 2 /* scale R distances by this much */
77#define G_SCALE 3 /* scale G distances by this much */
78#define B_SCALE 1 /* and B by this much */
79
80static const int c_scales[3] = { R_SCALE, G_SCALE, B_SCALE };
81#define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]]
82#define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]]
83#define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]]
84
85/*
86 * First we have the histogram data structure and routines for creating it.
87 *
88 * The number of bits of precision can be adjusted by changing these symbols.
89 * We recommend keeping 6 bits for G and 5 each for R and B.
90 * If you have plenty of memory and cycles, 6 bits all around gives marginally
91 * better results; if you are short of memory, 5 bits all around will save
92 * some space but degrade the results.
93 * To maintain a fully accurate histogram, we'd need to allocate a "long"
94 * (preferably unsigned long) for each cell. In practice this is overkill;
95 * we can get by with 16 bits per cell. Few of the cell counts will overflow,
96 * and clamping those that do overflow to the maximum value will give close-
97 * enough results. This reduces the recommended histogram size from 256Kb
98 * to 128Kb, which is a useful savings on PC-class machines.
99 * (In the second pass the histogram space is re-used for pixel mapping data;
100 * in that capacity, each cell must be able to store zero to the number of
101 * desired colors. 16 bits/cell is plenty for that too.)
102 * Since the JPEG code is intended to run in small memory model on 80x86
103 * machines, we can't just allocate the histogram in one chunk. Instead
104 * of a true 3-D array, we use a row of pointers to 2-D arrays. Each
105 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
106 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.
107 */
108
109#define MAXNUMCOLORS (MAXJSAMPLE + 1) /* maximum size of colormap */
110
111/* These will do the right thing for either R,G,B or B,G,R color order,
112 * but you may not like the results for other color orders.
113 */
114#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
115#define HIST_C1_BITS 6 /* bits of precision in G histogram */
116#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
117
118/* Number of elements along histogram axes. */
119#define HIST_C0_ELEMS (1 << HIST_C0_BITS)
120#define HIST_C1_ELEMS (1 << HIST_C1_BITS)
121#define HIST_C2_ELEMS (1 << HIST_C2_BITS)
122
123/* These are the amounts to shift an input value to get a histogram index. */
124#define C0_SHIFT (BITS_IN_JSAMPLE - HIST_C0_BITS)
125#define C1_SHIFT (BITS_IN_JSAMPLE - HIST_C1_BITS)
126#define C2_SHIFT (BITS_IN_JSAMPLE - HIST_C2_BITS)
127
128
129typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */
130
131typedef histcell *histptr; /* for pointers to histogram cells */
132
133typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
134typedef hist1d *hist2d; /* type for the 2nd-level pointers */
135typedef hist2d *hist3d; /* type for top-level pointer */
136
137
138/* Declarations for Floyd-Steinberg dithering.
139 *
140 * Errors are accumulated into the array fserrors[], at a resolution of
141 * 1/16th of a pixel count. The error at a given pixel is propagated
142 * to its not-yet-processed neighbors using the standard F-S fractions,
143 * ... (here) 7/16
144 * 3/16 5/16 1/16
145 * We work left-to-right on even rows, right-to-left on odd rows.
146 *
147 * We can get away with a single array (holding one row's worth of errors)
148 * by using it to store the current row's errors at pixel columns not yet
149 * processed, but the next row's errors at columns already processed. We
150 * need only a few extra variables to hold the errors immediately around the
151 * current column. (If we are lucky, those variables are in registers, but
152 * even if not, they're probably cheaper to access than array elements are.)
153 *
154 * The fserrors[] array has (#columns + 2) entries; the extra entry at
155 * each end saves us from special-casing the first and last pixels.
156 * Each entry is three values long, one value for each color component.
157 */
158
159#if BITS_IN_JSAMPLE == 8
160typedef INT16 FSERROR; /* 16 bits should be enough */
161typedef int LOCFSERROR; /* use 'int' for calculation temps */
162#else
163typedef JLONG FSERROR; /* may need more than 16 bits */
164typedef JLONG LOCFSERROR; /* be sure calculation temps are big enough */
165#endif
166
167typedef FSERROR *FSERRPTR; /* pointer to error array */
168
169
170/* Private subobject */
171
172typedef struct {
173 struct jpeg_color_quantizer pub; /* public fields */
174
175 /* Space for the eventually created colormap is stashed here */
176 JSAMPARRAY sv_colormap; /* colormap allocated at init time */
177 int desired; /* desired # of colors = size of colormap */
178
179 /* Variables for accumulating image statistics */
180 hist3d histogram; /* pointer to the histogram */
181
182 boolean needs_zeroed; /* TRUE if next pass must zero histogram */
183
184 /* Variables for Floyd-Steinberg dithering */
185 FSERRPTR fserrors; /* accumulated errors */
186 boolean on_odd_row; /* flag to remember which row we are on */
187 int *error_limiter; /* table for clamping the applied error */
188} my_cquantizer;
189
190typedef my_cquantizer *my_cquantize_ptr;
191
192
193/*
194 * Prescan some rows of pixels.
195 * In this module the prescan simply updates the histogram, which has been
196 * initialized to zeroes by start_pass.
197 * An output_buf parameter is required by the method signature, but no data
198 * is actually output (in fact the buffer controller is probably passing a
199 * NULL pointer).
200 */
201
202METHODDEF(void)
203prescan_quantize(j_decompress_ptr cinfo, JSAMPARRAY input_buf,
204 JSAMPARRAY output_buf, int num_rows)
205{
206 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
207 register JSAMPROW ptr;
208 register histptr histp;
209 register hist3d histogram = cquantize->histogram;
210 int row;
211 JDIMENSION col;
212 JDIMENSION width = cinfo->output_width;
213
214 for (row = 0; row < num_rows; row++) {
215 ptr = input_buf[row];
216 for (col = width; col > 0; col--) {
217 /* get pixel value and index into the histogram */
218 histp = &histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
219 [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
220 [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
221 /* increment, check for overflow and undo increment if so. */
222 if (++(*histp) <= 0)
223 (*histp)--;
224 ptr += 3;
225 }
226 }
227}
228
229
230/*
231 * Next we have the really interesting routines: selection of a colormap
232 * given the completed histogram.
233 * These routines work with a list of "boxes", each representing a rectangular
234 * subset of the input color space (to histogram precision).
235 */
236
237typedef struct {
238 /* The bounds of the box (inclusive); expressed as histogram indexes */
239 int c0min, c0max;
240 int c1min, c1max;
241 int c2min, c2max;
242 /* The volume (actually 2-norm) of the box */
243 JLONG volume;
244 /* The number of nonzero histogram cells within this box */
245 long colorcount;
246} box;
247
248typedef box *boxptr;
249
250
251LOCAL(boxptr)
252find_biggest_color_pop(boxptr boxlist, int numboxes)
253/* Find the splittable box with the largest color population */
254/* Returns NULL if no splittable boxes remain */
255{
256 register boxptr boxp;
257 register int i;
258 register long maxc = 0;
259 boxptr which = NULL;
260
261 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
262 if (boxp->colorcount > maxc && boxp->volume > 0) {
263 which = boxp;
264 maxc = boxp->colorcount;
265 }
266 }
267 return which;
268}
269
270
271LOCAL(boxptr)
272find_biggest_volume(boxptr boxlist, int numboxes)
273/* Find the splittable box with the largest (scaled) volume */
274/* Returns NULL if no splittable boxes remain */
275{
276 register boxptr boxp;
277 register int i;
278 register JLONG maxv = 0;
279 boxptr which = NULL;
280
281 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
282 if (boxp->volume > maxv) {
283 which = boxp;
284 maxv = boxp->volume;
285 }
286 }
287 return which;
288}
289
290
291LOCAL(void)
292update_box(j_decompress_ptr cinfo, boxptr boxp)
293/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
294/* and recompute its volume and population */
295{
296 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
297 hist3d histogram = cquantize->histogram;
298 histptr histp;
299 int c0, c1, c2;
300 int c0min, c0max, c1min, c1max, c2min, c2max;
301 JLONG dist0, dist1, dist2;
302 long ccount;
303
304 c0min = boxp->c0min; c0max = boxp->c0max;
305 c1min = boxp->c1min; c1max = boxp->c1max;
306 c2min = boxp->c2min; c2max = boxp->c2max;
307
308 if (c0max > c0min)
309 for (c0 = c0min; c0 <= c0max; c0++)
310 for (c1 = c1min; c1 <= c1max; c1++) {
311 histp = &histogram[c0][c1][c2min];
312 for (c2 = c2min; c2 <= c2max; c2++)
313 if (*histp++ != 0) {
314 boxp->c0min = c0min = c0;
315 goto have_c0min;
316 }
317 }
318have_c0min:
319 if (c0max > c0min)
320 for (c0 = c0max; c0 >= c0min; c0--)
321 for (c1 = c1min; c1 <= c1max; c1++) {
322 histp = &histogram[c0][c1][c2min];
323 for (c2 = c2min; c2 <= c2max; c2++)
324 if (*histp++ != 0) {
325 boxp->c0max = c0max = c0;
326 goto have_c0max;
327 }
328 }
329have_c0max:
330 if (c1max > c1min)
331 for (c1 = c1min; c1 <= c1max; c1++)
332 for (c0 = c0min; c0 <= c0max; c0++) {
333 histp = &histogram[c0][c1][c2min];
334 for (c2 = c2min; c2 <= c2max; c2++)
335 if (*histp++ != 0) {
336 boxp->c1min = c1min = c1;
337 goto have_c1min;
338 }
339 }
340have_c1min:
341 if (c1max > c1min)
342 for (c1 = c1max; c1 >= c1min; c1--)
343 for (c0 = c0min; c0 <= c0max; c0++) {
344 histp = &histogram[c0][c1][c2min];
345 for (c2 = c2min; c2 <= c2max; c2++)
346 if (*histp++ != 0) {
347 boxp->c1max = c1max = c1;
348 goto have_c1max;
349 }
350 }
351have_c1max:
352 if (c2max > c2min)
353 for (c2 = c2min; c2 <= c2max; c2++)
354 for (c0 = c0min; c0 <= c0max; c0++) {
355 histp = &histogram[c0][c1min][c2];
356 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
357 if (*histp != 0) {
358 boxp->c2min = c2min = c2;
359 goto have_c2min;
360 }
361 }
362have_c2min:
363 if (c2max > c2min)
364 for (c2 = c2max; c2 >= c2min; c2--)
365 for (c0 = c0min; c0 <= c0max; c0++) {
366 histp = &histogram[c0][c1min][c2];
367 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
368 if (*histp != 0) {
369 boxp->c2max = c2max = c2;
370 goto have_c2max;
371 }
372 }
373have_c2max:
374
375 /* Update box volume.
376 * We use 2-norm rather than real volume here; this biases the method
377 * against making long narrow boxes, and it has the side benefit that
378 * a box is splittable iff norm > 0.
379 * Since the differences are expressed in histogram-cell units,
380 * we have to shift back to JSAMPLE units to get consistent distances;
381 * after which, we scale according to the selected distance scale factors.
382 */
383 dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
384 dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
385 dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
386 boxp->volume = dist0 * dist0 + dist1 * dist1 + dist2 * dist2;
387
388 /* Now scan remaining volume of box and compute population */
389 ccount = 0;
390 for (c0 = c0min; c0 <= c0max; c0++)
391 for (c1 = c1min; c1 <= c1max; c1++) {
392 histp = &histogram[c0][c1][c2min];
393 for (c2 = c2min; c2 <= c2max; c2++, histp++)
394 if (*histp != 0) {
395 ccount++;
396 }
397 }
398 boxp->colorcount = ccount;
399}
400
401
402LOCAL(int)
403median_cut(j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
404 int desired_colors)
405/* Repeatedly select and split the largest box until we have enough boxes */
406{
407 int n, lb;
408 int c0, c1, c2, cmax;
409 register boxptr b1, b2;
410
411 while (numboxes < desired_colors) {
412 /* Select box to split.
413 * Current algorithm: by population for first half, then by volume.
414 */
415 if (numboxes * 2 <= desired_colors) {
416 b1 = find_biggest_color_pop(boxlist, numboxes);
417 } else {
418 b1 = find_biggest_volume(boxlist, numboxes);
419 }
420 if (b1 == NULL) /* no splittable boxes left! */
421 break;
422 b2 = &boxlist[numboxes]; /* where new box will go */
423 /* Copy the color bounds to the new box. */
424 b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
425 b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
426 /* Choose which axis to split the box on.
427 * Current algorithm: longest scaled axis.
428 * See notes in update_box about scaling distances.
429 */
430 c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
431 c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
432 c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
433 /* We want to break any ties in favor of green, then red, blue last.
434 * This code does the right thing for R,G,B or B,G,R color orders only.
435 */
436 if (rgb_red[cinfo->out_color_space] == 0) {
437 cmax = c1; n = 1;
438 if (c0 > cmax) { cmax = c0; n = 0; }
439 if (c2 > cmax) { n = 2; }
440 } else {
441 cmax = c1; n = 1;
442 if (c2 > cmax) { cmax = c2; n = 2; }
443 if (c0 > cmax) { n = 0; }
444 }
445 /* Choose split point along selected axis, and update box bounds.
446 * Current algorithm: split at halfway point.
447 * (Since the box has been shrunk to minimum volume,
448 * any split will produce two nonempty subboxes.)
449 * Note that lb value is max for lower box, so must be < old max.
450 */
451 switch (n) {
452 case 0:
453 lb = (b1->c0max + b1->c0min) / 2;
454 b1->c0max = lb;
455 b2->c0min = lb + 1;
456 break;
457 case 1:
458 lb = (b1->c1max + b1->c1min) / 2;
459 b1->c1max = lb;
460 b2->c1min = lb + 1;
461 break;
462 case 2:
463 lb = (b1->c2max + b1->c2min) / 2;
464 b1->c2max = lb;
465 b2->c2min = lb + 1;
466 break;
467 }
468 /* Update stats for boxes */
469 update_box(cinfo, b1);
470 update_box(cinfo, b2);
471 numboxes++;
472 }
473 return numboxes;
474}
475
476
477LOCAL(void)
478compute_color(j_decompress_ptr cinfo, boxptr boxp, int icolor)
479/* Compute representative color for a box, put it in colormap[icolor] */
480{
481 /* Current algorithm: mean weighted by pixels (not colors) */
482 /* Note it is important to get the rounding correct! */
483 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
484 hist3d histogram = cquantize->histogram;
485 histptr histp;
486 int c0, c1, c2;
487 int c0min, c0max, c1min, c1max, c2min, c2max;
488 long count;
489 long total = 0;
490 long c0total = 0;
491 long c1total = 0;
492 long c2total = 0;
493
494 c0min = boxp->c0min; c0max = boxp->c0max;
495 c1min = boxp->c1min; c1max = boxp->c1max;
496 c2min = boxp->c2min; c2max = boxp->c2max;
497
498 for (c0 = c0min; c0 <= c0max; c0++)
499 for (c1 = c1min; c1 <= c1max; c1++) {
500 histp = &histogram[c0][c1][c2min];
501 for (c2 = c2min; c2 <= c2max; c2++) {
502 if ((count = *histp++) != 0) {
503 total += count;
504 c0total += ((c0 << C0_SHIFT) + ((1 << C0_SHIFT) >> 1)) * count;
505 c1total += ((c1 << C1_SHIFT) + ((1 << C1_SHIFT) >> 1)) * count;
506 c2total += ((c2 << C2_SHIFT) + ((1 << C2_SHIFT) >> 1)) * count;
507 }
508 }
509 }
510
511 cinfo->colormap[0][icolor] = (JSAMPLE)((c0total + (total >> 1)) / total);
512 cinfo->colormap[1][icolor] = (JSAMPLE)((c1total + (total >> 1)) / total);
513 cinfo->colormap[2][icolor] = (JSAMPLE)((c2total + (total >> 1)) / total);
514}
515
516
517LOCAL(void)
518select_colors(j_decompress_ptr cinfo, int desired_colors)
519/* Master routine for color selection */
520{
521 boxptr boxlist;
522 int numboxes;
523 int i;
524
525 /* Allocate workspace for box list */
526 boxlist = (boxptr)(*cinfo->mem->alloc_small)
527 ((j_common_ptr)cinfo, JPOOL_IMAGE, desired_colors * sizeof(box));
528 /* Initialize one box containing whole space */
529 numboxes = 1;
530 boxlist[0].c0min = 0;
531 boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
532 boxlist[0].c1min = 0;
533 boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
534 boxlist[0].c2min = 0;
535 boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
536 /* Shrink it to actually-used volume and set its statistics */
537 update_box(cinfo, &boxlist[0]);
538 /* Perform median-cut to produce final box list */
539 numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
540 /* Compute the representative color for each box, fill colormap */
541 for (i = 0; i < numboxes; i++)
542 compute_color(cinfo, &boxlist[i], i);
543 cinfo->actual_number_of_colors = numboxes;
544 TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
545}
546
547
548/*
549 * These routines are concerned with the time-critical task of mapping input
550 * colors to the nearest color in the selected colormap.
551 *
552 * We re-use the histogram space as an "inverse color map", essentially a
553 * cache for the results of nearest-color searches. All colors within a
554 * histogram cell will be mapped to the same colormap entry, namely the one
555 * closest to the cell's center. This may not be quite the closest entry to
556 * the actual input color, but it's almost as good. A zero in the cache
557 * indicates we haven't found the nearest color for that cell yet; the array
558 * is cleared to zeroes before starting the mapping pass. When we find the
559 * nearest color for a cell, its colormap index plus one is recorded in the
560 * cache for future use. The pass2 scanning routines call fill_inverse_cmap
561 * when they need to use an unfilled entry in the cache.
562 *
563 * Our method of efficiently finding nearest colors is based on the "locally
564 * sorted search" idea described by Heckbert and on the incremental distance
565 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
566 * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
567 * the distances from a given colormap entry to each cell of the histogram can
568 * be computed quickly using an incremental method: the differences between
569 * distances to adjacent cells themselves differ by a constant. This allows a
570 * fairly fast implementation of the "brute force" approach of computing the
571 * distance from every colormap entry to every histogram cell. Unfortunately,
572 * it needs a work array to hold the best-distance-so-far for each histogram
573 * cell (because the inner loop has to be over cells, not colormap entries).
574 * The work array elements have to be JLONGs, so the work array would need
575 * 256Kb at our recommended precision. This is not feasible in DOS machines.
576 *
577 * To get around these problems, we apply Thomas' method to compute the
578 * nearest colors for only the cells within a small subbox of the histogram.
579 * The work array need be only as big as the subbox, so the memory usage
580 * problem is solved. Furthermore, we need not fill subboxes that are never
581 * referenced in pass2; many images use only part of the color gamut, so a
582 * fair amount of work is saved. An additional advantage of this
583 * approach is that we can apply Heckbert's locality criterion to quickly
584 * eliminate colormap entries that are far away from the subbox; typically
585 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
586 * and we need not compute their distances to individual cells in the subbox.
587 * The speed of this approach is heavily influenced by the subbox size: too
588 * small means too much overhead, too big loses because Heckbert's criterion
589 * can't eliminate as many colormap entries. Empirically the best subbox
590 * size seems to be about 1/512th of the histogram (1/8th in each direction).
591 *
592 * Thomas' article also describes a refined method which is asymptotically
593 * faster than the brute-force method, but it is also far more complex and
594 * cannot efficiently be applied to small subboxes. It is therefore not
595 * useful for programs intended to be portable to DOS machines. On machines
596 * with plenty of memory, filling the whole histogram in one shot with Thomas'
597 * refined method might be faster than the present code --- but then again,
598 * it might not be any faster, and it's certainly more complicated.
599 */
600
601
602/* log2(histogram cells in update box) for each axis; this can be adjusted */
603#define BOX_C0_LOG (HIST_C0_BITS - 3)
604#define BOX_C1_LOG (HIST_C1_BITS - 3)
605#define BOX_C2_LOG (HIST_C2_BITS - 3)
606
607#define BOX_C0_ELEMS (1 << BOX_C0_LOG) /* # of hist cells in update box */
608#define BOX_C1_ELEMS (1 << BOX_C1_LOG)
609#define BOX_C2_ELEMS (1 << BOX_C2_LOG)
610
611#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
612#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
613#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)
614
615
616/*
617 * The next three routines implement inverse colormap filling. They could
618 * all be folded into one big routine, but splitting them up this way saves
619 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
620 * and may allow some compilers to produce better code by registerizing more
621 * inner-loop variables.
622 */
623
624LOCAL(int)
625find_nearby_colors(j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
626 JSAMPLE colorlist[])
627/* Locate the colormap entries close enough to an update box to be candidates
628 * for the nearest entry to some cell(s) in the update box. The update box
629 * is specified by the center coordinates of its first cell. The number of
630 * candidate colormap entries is returned, and their colormap indexes are
631 * placed in colorlist[].
632 * This routine uses Heckbert's "locally sorted search" criterion to select
633 * the colors that need further consideration.
634 */
635{
636 int numcolors = cinfo->actual_number_of_colors;
637 int maxc0, maxc1, maxc2;
638 int centerc0, centerc1, centerc2;
639 int i, x, ncolors;
640 JLONG minmaxdist, min_dist, max_dist, tdist;
641 JLONG mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
642
643 /* Compute true coordinates of update box's upper corner and center.
644 * Actually we compute the coordinates of the center of the upper-corner
645 * histogram cell, which are the upper bounds of the volume we care about.
646 * Note that since ">>" rounds down, the "center" values may be closer to
647 * min than to max; hence comparisons to them must be "<=", not "<".
648 */
649 maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
650 centerc0 = (minc0 + maxc0) >> 1;
651 maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
652 centerc1 = (minc1 + maxc1) >> 1;
653 maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
654 centerc2 = (minc2 + maxc2) >> 1;
655
656 /* For each color in colormap, find:
657 * 1. its minimum squared-distance to any point in the update box
658 * (zero if color is within update box);
659 * 2. its maximum squared-distance to any point in the update box.
660 * Both of these can be found by considering only the corners of the box.
661 * We save the minimum distance for each color in mindist[];
662 * only the smallest maximum distance is of interest.
663 */
664 minmaxdist = 0x7FFFFFFFL;
665
666 for (i = 0; i < numcolors; i++) {
667 /* We compute the squared-c0-distance term, then add in the other two. */
668 x = GETJSAMPLE(cinfo->colormap[0][i]);
669 if (x < minc0) {
670 tdist = (x - minc0) * C0_SCALE;
671 min_dist = tdist * tdist;
672 tdist = (x - maxc0) * C0_SCALE;
673 max_dist = tdist * tdist;
674 } else if (x > maxc0) {
675 tdist = (x - maxc0) * C0_SCALE;
676 min_dist = tdist * tdist;
677 tdist = (x - minc0) * C0_SCALE;
678 max_dist = tdist * tdist;
679 } else {
680 /* within cell range so no contribution to min_dist */
681 min_dist = 0;
682 if (x <= centerc0) {
683 tdist = (x - maxc0) * C0_SCALE;
684 max_dist = tdist * tdist;
685 } else {
686 tdist = (x - minc0) * C0_SCALE;
687 max_dist = tdist * tdist;
688 }
689 }
690
691 x = GETJSAMPLE(cinfo->colormap[1][i]);
692 if (x < minc1) {
693 tdist = (x - minc1) * C1_SCALE;
694 min_dist += tdist * tdist;
695 tdist = (x - maxc1) * C1_SCALE;
696 max_dist += tdist * tdist;
697 } else if (x > maxc1) {
698 tdist = (x - maxc1) * C1_SCALE;
699 min_dist += tdist * tdist;
700 tdist = (x - minc1) * C1_SCALE;
701 max_dist += tdist * tdist;
702 } else {
703 /* within cell range so no contribution to min_dist */
704 if (x <= centerc1) {
705 tdist = (x - maxc1) * C1_SCALE;
706 max_dist += tdist * tdist;
707 } else {
708 tdist = (x - minc1) * C1_SCALE;
709 max_dist += tdist * tdist;
710 }
711 }
712
713 x = GETJSAMPLE(cinfo->colormap[2][i]);
714 if (x < minc2) {
715 tdist = (x - minc2) * C2_SCALE;
716 min_dist += tdist * tdist;
717 tdist = (x - maxc2) * C2_SCALE;
718 max_dist += tdist * tdist;
719 } else if (x > maxc2) {
720 tdist = (x - maxc2) * C2_SCALE;
721 min_dist += tdist * tdist;
722 tdist = (x - minc2) * C2_SCALE;
723 max_dist += tdist * tdist;
724 } else {
725 /* within cell range so no contribution to min_dist */
726 if (x <= centerc2) {
727 tdist = (x - maxc2) * C2_SCALE;
728 max_dist += tdist * tdist;
729 } else {
730 tdist = (x - minc2) * C2_SCALE;
731 max_dist += tdist * tdist;
732 }
733 }
734
735 mindist[i] = min_dist; /* save away the results */
736 if (max_dist < minmaxdist)
737 minmaxdist = max_dist;
738 }
739
740 /* Now we know that no cell in the update box is more than minmaxdist
741 * away from some colormap entry. Therefore, only colors that are
742 * within minmaxdist of some part of the box need be considered.
743 */
744 ncolors = 0;
745 for (i = 0; i < numcolors; i++) {
746 if (mindist[i] <= minmaxdist)
747 colorlist[ncolors++] = (JSAMPLE)i;
748 }
749 return ncolors;
750}
751
752
753LOCAL(void)
754find_best_colors(j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
755 int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
756/* Find the closest colormap entry for each cell in the update box,
757 * given the list of candidate colors prepared by find_nearby_colors.
758 * Return the indexes of the closest entries in the bestcolor[] array.
759 * This routine uses Thomas' incremental distance calculation method to
760 * find the distance from a colormap entry to successive cells in the box.
761 */
762{
763 int ic0, ic1, ic2;
764 int i, icolor;
765 register JLONG *bptr; /* pointer into bestdist[] array */
766 JSAMPLE *cptr; /* pointer into bestcolor[] array */
767 JLONG dist0, dist1; /* initial distance values */
768 register JLONG dist2; /* current distance in inner loop */
769 JLONG xx0, xx1; /* distance increments */
770 register JLONG xx2;
771 JLONG inc0, inc1, inc2; /* initial values for increments */
772 /* This array holds the distance to the nearest-so-far color for each cell */
773 JLONG bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
774
775 /* Initialize best-distance for each cell of the update box */
776 bptr = bestdist;
777 for (i = BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS - 1; i >= 0; i--)
778 *bptr++ = 0x7FFFFFFFL;
779
780 /* For each color selected by find_nearby_colors,
781 * compute its distance to the center of each cell in the box.
782 * If that's less than best-so-far, update best distance and color number.
783 */
784
785 /* Nominal steps between cell centers ("x" in Thomas article) */
786#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
787#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
788#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
789
790 for (i = 0; i < numcolors; i++) {
791 icolor = GETJSAMPLE(colorlist[i]);
792 /* Compute (square of) distance from minc0/c1/c2 to this color */
793 inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
794 dist0 = inc0 * inc0;
795 inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
796 dist0 += inc1 * inc1;
797 inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
798 dist0 += inc2 * inc2;
799 /* Form the initial difference increments */
800 inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
801 inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
802 inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
803 /* Now loop over all cells in box, updating distance per Thomas method */
804 bptr = bestdist;
805 cptr = bestcolor;
806 xx0 = inc0;
807 for (ic0 = BOX_C0_ELEMS - 1; ic0 >= 0; ic0--) {
808 dist1 = dist0;
809 xx1 = inc1;
810 for (ic1 = BOX_C1_ELEMS - 1; ic1 >= 0; ic1--) {
811 dist2 = dist1;
812 xx2 = inc2;
813 for (ic2 = BOX_C2_ELEMS - 1; ic2 >= 0; ic2--) {
814 if (dist2 < *bptr) {
815 *bptr = dist2;
816 *cptr = (JSAMPLE)icolor;
817 }
818 dist2 += xx2;
819 xx2 += 2 * STEP_C2 * STEP_C2;
820 bptr++;
821 cptr++;
822 }
823 dist1 += xx1;
824 xx1 += 2 * STEP_C1 * STEP_C1;
825 }
826 dist0 += xx0;
827 xx0 += 2 * STEP_C0 * STEP_C0;
828 }
829 }
830}
831
832
833LOCAL(void)
834fill_inverse_cmap(j_decompress_ptr cinfo, int c0, int c1, int c2)
835/* Fill the inverse-colormap entries in the update box that contains */
836/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
837/* we can fill as many others as we wish.) */
838{
839 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
840 hist3d histogram = cquantize->histogram;
841 int minc0, minc1, minc2; /* lower left corner of update box */
842 int ic0, ic1, ic2;
843 register JSAMPLE *cptr; /* pointer into bestcolor[] array */
844 register histptr cachep; /* pointer into main cache array */
845 /* This array lists the candidate colormap indexes. */
846 JSAMPLE colorlist[MAXNUMCOLORS];
847 int numcolors; /* number of candidate colors */
848 /* This array holds the actually closest colormap index for each cell. */
849 JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
850
851 /* Convert cell coordinates to update box ID */
852 c0 >>= BOX_C0_LOG;
853 c1 >>= BOX_C1_LOG;
854 c2 >>= BOX_C2_LOG;
855
856 /* Compute true coordinates of update box's origin corner.
857 * Actually we compute the coordinates of the center of the corner
858 * histogram cell, which are the lower bounds of the volume we care about.
859 */
860 minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
861 minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
862 minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
863
864 /* Determine which colormap entries are close enough to be candidates
865 * for the nearest entry to some cell in the update box.
866 */
867 numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
868
869 /* Determine the actually nearest colors. */
870 find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
871 bestcolor);
872
873 /* Save the best color numbers (plus 1) in the main cache array */
874 c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
875 c1 <<= BOX_C1_LOG;
876 c2 <<= BOX_C2_LOG;
877 cptr = bestcolor;
878 for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
879 for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
880 cachep = &histogram[c0 + ic0][c1 + ic1][c2];
881 for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
882 *cachep++ = (histcell)(GETJSAMPLE(*cptr++) + 1);
883 }
884 }
885 }
886}
887
888
889/*
890 * Map some rows of pixels to the output colormapped representation.
891 */
892
893METHODDEF(void)
894pass2_no_dither(j_decompress_ptr cinfo, JSAMPARRAY input_buf,
895 JSAMPARRAY output_buf, int num_rows)
896/* This version performs no dithering */
897{
898 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
899 hist3d histogram = cquantize->histogram;
900 register JSAMPROW inptr, outptr;
901 register histptr cachep;
902 register int c0, c1, c2;
903 int row;
904 JDIMENSION col;
905 JDIMENSION width = cinfo->output_width;
906
907 for (row = 0; row < num_rows; row++) {
908 inptr = input_buf[row];
909 outptr = output_buf[row];
910 for (col = width; col > 0; col--) {
911 /* get pixel value and index into the cache */
912 c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
913 c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
914 c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
915 cachep = &histogram[c0][c1][c2];
916 /* If we have not seen this color before, find nearest colormap entry */
917 /* and update the cache */
918 if (*cachep == 0)
919 fill_inverse_cmap(cinfo, c0, c1, c2);
920 /* Now emit the colormap index for this cell */
921 *outptr++ = (JSAMPLE)(*cachep - 1);
922 }
923 }
924}
925
926
927METHODDEF(void)
928pass2_fs_dither(j_decompress_ptr cinfo, JSAMPARRAY input_buf,
929 JSAMPARRAY output_buf, int num_rows)
930/* This version performs Floyd-Steinberg dithering */
931{
932 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
933 hist3d histogram = cquantize->histogram;
934 register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
935 LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
936 LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
937 register FSERRPTR errorptr; /* => fserrors[] at column before current */
938 JSAMPROW inptr; /* => current input pixel */
939 JSAMPROW outptr; /* => current output pixel */
940 histptr cachep;
941 int dir; /* +1 or -1 depending on direction */
942 int dir3; /* 3*dir, for advancing inptr & errorptr */
943 int row;
944 JDIMENSION col;
945 JDIMENSION width = cinfo->output_width;
946 JSAMPLE *range_limit = cinfo->sample_range_limit;
947 int *error_limit = cquantize->error_limiter;
948 JSAMPROW colormap0 = cinfo->colormap[0];
949 JSAMPROW colormap1 = cinfo->colormap[1];
950 JSAMPROW colormap2 = cinfo->colormap[2];
951 SHIFT_TEMPS
952
953 for (row = 0; row < num_rows; row++) {
954 inptr = input_buf[row];
955 outptr = output_buf[row];
956 if (cquantize->on_odd_row) {
957 /* work right to left in this row */
958 inptr += (width - 1) * 3; /* so point to rightmost pixel */
959 outptr += width - 1;
960 dir = -1;
961 dir3 = -3;
962 errorptr = cquantize->fserrors + (width + 1) * 3; /* => entry after last column */
963 cquantize->on_odd_row = FALSE; /* flip for next time */
964 } else {
965 /* work left to right in this row */
966 dir = 1;
967 dir3 = 3;
968 errorptr = cquantize->fserrors; /* => entry before first real column */
969 cquantize->on_odd_row = TRUE; /* flip for next time */
970 }
971 /* Preset error values: no error propagated to first pixel from left */
972 cur0 = cur1 = cur2 = 0;
973 /* and no error propagated to row below yet */
974 belowerr0 = belowerr1 = belowerr2 = 0;
975 bpreverr0 = bpreverr1 = bpreverr2 = 0;
976
977 for (col = width; col > 0; col--) {
978 /* curN holds the error propagated from the previous pixel on the
979 * current line. Add the error propagated from the previous line
980 * to form the complete error correction term for this pixel, and
981 * round the error term (which is expressed * 16) to an integer.
982 * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
983 * for either sign of the error value.
984 * Note: errorptr points to *previous* column's array entry.
985 */
986 cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3 + 0] + 8, 4);
987 cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3 + 1] + 8, 4);
988 cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3 + 2] + 8, 4);
989 /* Limit the error using transfer function set by init_error_limit.
990 * See comments with init_error_limit for rationale.
991 */
992 cur0 = error_limit[cur0];
993 cur1 = error_limit[cur1];
994 cur2 = error_limit[cur2];
995 /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
996 * The maximum error is +- MAXJSAMPLE (or less with error limiting);
997 * this sets the required size of the range_limit array.
998 */
999 cur0 += GETJSAMPLE(inptr[0]);
1000 cur1 += GETJSAMPLE(inptr[1]);
1001 cur2 += GETJSAMPLE(inptr[2]);
1002 cur0 = GETJSAMPLE(range_limit[cur0]);
1003 cur1 = GETJSAMPLE(range_limit[cur1]);
1004 cur2 = GETJSAMPLE(range_limit[cur2]);
1005 /* Index into the cache with adjusted pixel value */
1006 cachep =
1007 &histogram[cur0 >> C0_SHIFT][cur1 >> C1_SHIFT][cur2 >> C2_SHIFT];
1008 /* If we have not seen this color before, find nearest colormap */
1009 /* entry and update the cache */
1010 if (*cachep == 0)
1011 fill_inverse_cmap(cinfo, cur0 >> C0_SHIFT, cur1 >> C1_SHIFT,
1012 cur2 >> C2_SHIFT);
1013 /* Now emit the colormap index for this cell */
1014 {
1015 register int pixcode = *cachep - 1;
1016 *outptr = (JSAMPLE)pixcode;
1017 /* Compute representation error for this pixel */
1018 cur0 -= GETJSAMPLE(colormap0[pixcode]);
1019 cur1 -= GETJSAMPLE(colormap1[pixcode]);
1020 cur2 -= GETJSAMPLE(colormap2[pixcode]);
1021 }
1022 /* Compute error fractions to be propagated to adjacent pixels.
1023 * Add these into the running sums, and simultaneously shift the
1024 * next-line error sums left by 1 column.
1025 */
1026 {
1027 register LOCFSERROR bnexterr;
1028
1029 bnexterr = cur0; /* Process component 0 */
1030 errorptr[0] = (FSERROR)(bpreverr0 + cur0 * 3);
1031 bpreverr0 = belowerr0 + cur0 * 5;
1032 belowerr0 = bnexterr;
1033 cur0 *= 7;
1034 bnexterr = cur1; /* Process component 1 */
1035 errorptr[1] = (FSERROR)(bpreverr1 + cur1 * 3);
1036 bpreverr1 = belowerr1 + cur1 * 5;
1037 belowerr1 = bnexterr;
1038 cur1 *= 7;
1039 bnexterr = cur2; /* Process component 2 */
1040 errorptr[2] = (FSERROR)(bpreverr2 + cur2 * 3);
1041 bpreverr2 = belowerr2 + cur2 * 5;
1042 belowerr2 = bnexterr;
1043 cur2 *= 7;
1044 }
1045 /* At this point curN contains the 7/16 error value to be propagated
1046 * to the next pixel on the current line, and all the errors for the
1047 * next line have been shifted over. We are therefore ready to move on.
1048 */
1049 inptr += dir3; /* Advance pixel pointers to next column */
1050 outptr += dir;
1051 errorptr += dir3; /* advance errorptr to current column */
1052 }
1053 /* Post-loop cleanup: we must unload the final error values into the
1054 * final fserrors[] entry. Note we need not unload belowerrN because
1055 * it is for the dummy column before or after the actual array.
1056 */
1057 errorptr[0] = (FSERROR)bpreverr0; /* unload prev errs into array */
1058 errorptr[1] = (FSERROR)bpreverr1;
1059 errorptr[2] = (FSERROR)bpreverr2;
1060 }
1061}
1062
1063
1064/*
1065 * Initialize the error-limiting transfer function (lookup table).
1066 * The raw F-S error computation can potentially compute error values of up to
1067 * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
1068 * much less, otherwise obviously wrong pixels will be created. (Typical
1069 * effects include weird fringes at color-area boundaries, isolated bright
1070 * pixels in a dark area, etc.) The standard advice for avoiding this problem
1071 * is to ensure that the "corners" of the color cube are allocated as output
1072 * colors; then repeated errors in the same direction cannot cause cascading
1073 * error buildup. However, that only prevents the error from getting
1074 * completely out of hand; Aaron Giles reports that error limiting improves
1075 * the results even with corner colors allocated.
1076 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1077 * well, but the smoother transfer function used below is even better. Thanks
1078 * to Aaron Giles for this idea.
1079 */
1080
1081LOCAL(void)
1082init_error_limit(j_decompress_ptr cinfo)
1083/* Allocate and fill in the error_limiter table */
1084{
1085 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
1086 int *table;
1087 int in, out;
1088
1089 table = (int *)(*cinfo->mem->alloc_small)
1090 ((j_common_ptr)cinfo, JPOOL_IMAGE, (MAXJSAMPLE * 2 + 1) * sizeof(int));
1091 table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1092 cquantize->error_limiter = table;
1093
1094#define STEPSIZE ((MAXJSAMPLE + 1) / 16)
1095 /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1096 out = 0;
1097 for (in = 0; in < STEPSIZE; in++, out++) {
1098 table[in] = out; table[-in] = -out;
1099 }
1100 /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1101 for (; in < STEPSIZE * 3; in++, out += (in & 1) ? 0 : 1) {
1102 table[in] = out; table[-in] = -out;
1103 }
1104 /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1105 for (; in <= MAXJSAMPLE; in++) {
1106 table[in] = out; table[-in] = -out;
1107 }
1108#undef STEPSIZE
1109}
1110
1111
1112/*
1113 * Finish up at the end of each pass.
1114 */
1115
1116METHODDEF(void)
1117finish_pass1(j_decompress_ptr cinfo)
1118{
1119 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
1120
1121 /* Select the representative colors and fill in cinfo->colormap */
1122 cinfo->colormap = cquantize->sv_colormap;
1123 select_colors(cinfo, cquantize->desired);
1124 /* Force next pass to zero the color index table */
1125 cquantize->needs_zeroed = TRUE;
1126}
1127
1128
1129METHODDEF(void)
1130finish_pass2(j_decompress_ptr cinfo)
1131{
1132 /* no work */
1133}
1134
1135
1136/*
1137 * Initialize for each processing pass.
1138 */
1139
1140METHODDEF(void)
1141start_pass_2_quant(j_decompress_ptr cinfo, boolean is_pre_scan)
1142{
1143 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
1144 hist3d histogram = cquantize->histogram;
1145 int i;
1146
1147 /* Only F-S dithering or no dithering is supported. */
1148 /* If user asks for ordered dither, give him F-S. */
1149 if (cinfo->dither_mode != JDITHER_NONE)
1150 cinfo->dither_mode = JDITHER_FS;
1151
1152 if (is_pre_scan) {
1153 /* Set up method pointers */
1154 cquantize->pub.color_quantize = prescan_quantize;
1155 cquantize->pub.finish_pass = finish_pass1;
1156 cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1157 } else {
1158 /* Set up method pointers */
1159 if (cinfo->dither_mode == JDITHER_FS)
1160 cquantize->pub.color_quantize = pass2_fs_dither;
1161 else
1162 cquantize->pub.color_quantize = pass2_no_dither;
1163 cquantize->pub.finish_pass = finish_pass2;
1164
1165 /* Make sure color count is acceptable */
1166 i = cinfo->actual_number_of_colors;
1167 if (i < 1)
1168 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1169 if (i > MAXNUMCOLORS)
1170 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1171
1172 if (cinfo->dither_mode == JDITHER_FS) {
1173 size_t arraysize =
1174 (size_t)((cinfo->output_width + 2) * (3 * sizeof(FSERROR)));
1175 /* Allocate Floyd-Steinberg workspace if we didn't already. */
1176 if (cquantize->fserrors == NULL)
1177 cquantize->fserrors = (FSERRPTR)(*cinfo->mem->alloc_large)
1178 ((j_common_ptr)cinfo, JPOOL_IMAGE, arraysize);
1179 /* Initialize the propagated errors to zero. */
1180 jzero_far((void *)cquantize->fserrors, arraysize);
1181 /* Make the error-limit table if we didn't already. */
1182 if (cquantize->error_limiter == NULL)
1183 init_error_limit(cinfo);
1184 cquantize->on_odd_row = FALSE;
1185 }
1186
1187 }
1188 /* Zero the histogram or inverse color map, if necessary */
1189 if (cquantize->needs_zeroed) {
1190 for (i = 0; i < HIST_C0_ELEMS; i++) {
1191 jzero_far((void *)histogram[i],
1192 HIST_C1_ELEMS * HIST_C2_ELEMS * sizeof(histcell));
1193 }
1194 cquantize->needs_zeroed = FALSE;
1195 }
1196}
1197
1198
1199/*
1200 * Switch to a new external colormap between output passes.
1201 */
1202
1203METHODDEF(void)
1204new_color_map_2_quant(j_decompress_ptr cinfo)
1205{
1206 my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
1207
1208 /* Reset the inverse color map */
1209 cquantize->needs_zeroed = TRUE;
1210}
1211
1212
1213/*
1214 * Module initialization routine for 2-pass color quantization.
1215 */
1216
1217GLOBAL(void)
1218jinit_2pass_quantizer(j_decompress_ptr cinfo)
1219{
1220 my_cquantize_ptr cquantize;
1221 int i;
1222
1223 cquantize = (my_cquantize_ptr)
1224 (*cinfo->mem->alloc_small) ((j_common_ptr)cinfo, JPOOL_IMAGE,
1225 sizeof(my_cquantizer));
1226 cinfo->cquantize = (struct jpeg_color_quantizer *)cquantize;
1227 cquantize->pub.start_pass = start_pass_2_quant;
1228 cquantize->pub.new_color_map = new_color_map_2_quant;
1229 cquantize->fserrors = NULL; /* flag optional arrays not allocated */
1230 cquantize->error_limiter = NULL;
1231
1232 /* Make sure jdmaster didn't give me a case I can't handle */
1233 if (cinfo->out_color_components != 3)
1234 ERREXIT(cinfo, JERR_NOTIMPL);
1235
1236 /* Allocate the histogram/inverse colormap storage */
1237 cquantize->histogram = (hist3d)(*cinfo->mem->alloc_small)
1238 ((j_common_ptr)cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * sizeof(hist2d));
1239 for (i = 0; i < HIST_C0_ELEMS; i++) {
1240 cquantize->histogram[i] = (hist2d)(*cinfo->mem->alloc_large)
1241 ((j_common_ptr)cinfo, JPOOL_IMAGE,
1242 HIST_C1_ELEMS * HIST_C2_ELEMS * sizeof(histcell));
1243 }
1244 cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1245
1246 /* Allocate storage for the completed colormap, if required.
1247 * We do this now since it may affect the memory manager's space
1248 * calculations.
1249 */
1250 if (cinfo->enable_2pass_quant) {
1251 /* Make sure color count is acceptable */
1252 int desired = cinfo->desired_number_of_colors;
1253 /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1254 if (desired < 8)
1255 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1256 /* Make sure colormap indexes can be represented by JSAMPLEs */
1257 if (desired > MAXNUMCOLORS)
1258 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1259 cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1260 ((j_common_ptr)cinfo, JPOOL_IMAGE, (JDIMENSION)desired, (JDIMENSION)3);
1261 cquantize->desired = desired;
1262 } else
1263 cquantize->sv_colormap = NULL;
1264
1265 /* Only F-S dithering or no dithering is supported. */
1266 /* If user asks for ordered dither, give him F-S. */
1267 if (cinfo->dither_mode != JDITHER_NONE)
1268 cinfo->dither_mode = JDITHER_FS;
1269
1270 /* Allocate Floyd-Steinberg workspace if necessary.
1271 * This isn't really needed until pass 2, but again it may affect the memory
1272 * manager's space calculations. Although we will cope with a later change
1273 * in dither_mode, we do not promise to honor max_memory_to_use if
1274 * dither_mode changes.
1275 */
1276 if (cinfo->dither_mode == JDITHER_FS) {
1277 cquantize->fserrors = (FSERRPTR)(*cinfo->mem->alloc_large)
1278 ((j_common_ptr)cinfo, JPOOL_IMAGE,
1279 (size_t)((cinfo->output_width + 2) * (3 * sizeof(FSERROR))));
1280 /* Might as well create the error-limiting table too. */
1281 init_error_limit(cinfo);
1282 }
1283}
1284
1285#endif /* QUANT_2PASS_SUPPORTED */
1286