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 }
318 have_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 }
329 have_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 }
340 have_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 }
351 have_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 }
362 have_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 }
373 have_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 }
441 else {
442 cmax = c1; n = 1;
443 if (c2 > cmax) { cmax = c2; n = 2; }
444 if (c0 > cmax) { n = 0; }
445 }
446 /* Choose split point along selected axis, and update box bounds.
447 * Current algorithm: split at halfway point.
448 * (Since the box has been shrunk to minimum volume,
449 * any split will produce two nonempty subboxes.)
450 * Note that lb value is max for lower box, so must be < old max.
451 */
452 switch (n) {
453 case 0:
454 lb = (b1->c0max + b1->c0min) / 2;
455 b1->c0max = lb;
456 b2->c0min = lb+1;
457 break;
458 case 1:
459 lb = (b1->c1max + b1->c1min) / 2;
460 b1->c1max = lb;
461 b2->c1min = lb+1;
462 break;
463 case 2:
464 lb = (b1->c2max + b1->c2min) / 2;
465 b1->c2max = lb;
466 b2->c2min = lb+1;
467 break;
468 }
469 /* Update stats for boxes */
470 update_box(cinfo, b1);
471 update_box(cinfo, b2);
472 numboxes++;
473 }
474 return numboxes;
475}
476
477
478LOCAL(void)
479compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
480/* Compute representative color for a box, put it in colormap[icolor] */
481{
482 /* Current algorithm: mean weighted by pixels (not colors) */
483 /* Note it is important to get the rounding correct! */
484 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
485 hist3d histogram = cquantize->histogram;
486 histptr histp;
487 int c0,c1,c2;
488 int c0min,c0max,c1min,c1max,c2min,c2max;
489 long count;
490 long total = 0;
491 long c0total = 0;
492 long c1total = 0;
493 long c2total = 0;
494
495 c0min = boxp->c0min; c0max = boxp->c0max;
496 c1min = boxp->c1min; c1max = boxp->c1max;
497 c2min = boxp->c2min; c2max = boxp->c2max;
498
499 for (c0 = c0min; c0 <= c0max; c0++)
500 for (c1 = c1min; c1 <= c1max; c1++) {
501 histp = & histogram[c0][c1][c2min];
502 for (c2 = c2min; c2 <= c2max; c2++) {
503 if ((count = *histp++) != 0) {
504 total += count;
505 c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
506 c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
507 c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
508 }
509 }
510 }
511
512 cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
513 cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
514 cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
515}
516
517
518LOCAL(void)
519select_colors (j_decompress_ptr cinfo, int desired_colors)
520/* Master routine for color selection */
521{
522 boxptr boxlist;
523 int numboxes;
524 int i;
525
526 /* Allocate workspace for box list */
527 boxlist = (boxptr) (*cinfo->mem->alloc_small)
528 ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * sizeof(box));
529 /* Initialize one box containing whole space */
530 numboxes = 1;
531 boxlist[0].c0min = 0;
532 boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
533 boxlist[0].c1min = 0;
534 boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
535 boxlist[0].c2min = 0;
536 boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
537 /* Shrink it to actually-used volume and set its statistics */
538 update_box(cinfo, & boxlist[0]);
539 /* Perform median-cut to produce final box list */
540 numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
541 /* Compute the representative color for each box, fill colormap */
542 for (i = 0; i < numboxes; i++)
543 compute_color(cinfo, & boxlist[i], i);
544 cinfo->actual_number_of_colors = numboxes;
545 TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
546}
547
548
549/*
550 * These routines are concerned with the time-critical task of mapping input
551 * colors to the nearest color in the selected colormap.
552 *
553 * We re-use the histogram space as an "inverse color map", essentially a
554 * cache for the results of nearest-color searches. All colors within a
555 * histogram cell will be mapped to the same colormap entry, namely the one
556 * closest to the cell's center. This may not be quite the closest entry to
557 * the actual input color, but it's almost as good. A zero in the cache
558 * indicates we haven't found the nearest color for that cell yet; the array
559 * is cleared to zeroes before starting the mapping pass. When we find the
560 * nearest color for a cell, its colormap index plus one is recorded in the
561 * cache for future use. The pass2 scanning routines call fill_inverse_cmap
562 * when they need to use an unfilled entry in the cache.
563 *
564 * Our method of efficiently finding nearest colors is based on the "locally
565 * sorted search" idea described by Heckbert and on the incremental distance
566 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
567 * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
568 * the distances from a given colormap entry to each cell of the histogram can
569 * be computed quickly using an incremental method: the differences between
570 * distances to adjacent cells themselves differ by a constant. This allows a
571 * fairly fast implementation of the "brute force" approach of computing the
572 * distance from every colormap entry to every histogram cell. Unfortunately,
573 * it needs a work array to hold the best-distance-so-far for each histogram
574 * cell (because the inner loop has to be over cells, not colormap entries).
575 * The work array elements have to be JLONGs, so the work array would need
576 * 256Kb at our recommended precision. This is not feasible in DOS machines.
577 *
578 * To get around these problems, we apply Thomas' method to compute the
579 * nearest colors for only the cells within a small subbox of the histogram.
580 * The work array need be only as big as the subbox, so the memory usage
581 * problem is solved. Furthermore, we need not fill subboxes that are never
582 * referenced in pass2; many images use only part of the color gamut, so a
583 * fair amount of work is saved. An additional advantage of this
584 * approach is that we can apply Heckbert's locality criterion to quickly
585 * eliminate colormap entries that are far away from the subbox; typically
586 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
587 * and we need not compute their distances to individual cells in the subbox.
588 * The speed of this approach is heavily influenced by the subbox size: too
589 * small means too much overhead, too big loses because Heckbert's criterion
590 * can't eliminate as many colormap entries. Empirically the best subbox
591 * size seems to be about 1/512th of the histogram (1/8th in each direction).
592 *
593 * Thomas' article also describes a refined method which is asymptotically
594 * faster than the brute-force method, but it is also far more complex and
595 * cannot efficiently be applied to small subboxes. It is therefore not
596 * useful for programs intended to be portable to DOS machines. On machines
597 * with plenty of memory, filling the whole histogram in one shot with Thomas'
598 * refined method might be faster than the present code --- but then again,
599 * it might not be any faster, and it's certainly more complicated.
600 */
601
602
603/* log2(histogram cells in update box) for each axis; this can be adjusted */
604#define BOX_C0_LOG (HIST_C0_BITS-3)
605#define BOX_C1_LOG (HIST_C1_BITS-3)
606#define BOX_C2_LOG (HIST_C2_BITS-3)
607
608#define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */
609#define BOX_C1_ELEMS (1<<BOX_C1_LOG)
610#define BOX_C2_ELEMS (1<<BOX_C2_LOG)
611
612#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
613#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
614#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)
615
616
617/*
618 * The next three routines implement inverse colormap filling. They could
619 * all be folded into one big routine, but splitting them up this way saves
620 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
621 * and may allow some compilers to produce better code by registerizing more
622 * inner-loop variables.
623 */
624
625LOCAL(int)
626find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
627 JSAMPLE colorlist[])
628/* Locate the colormap entries close enough to an update box to be candidates
629 * for the nearest entry to some cell(s) in the update box. The update box
630 * is specified by the center coordinates of its first cell. The number of
631 * candidate colormap entries is returned, and their colormap indexes are
632 * placed in colorlist[].
633 * This routine uses Heckbert's "locally sorted search" criterion to select
634 * the colors that need further consideration.
635 */
636{
637 int numcolors = cinfo->actual_number_of_colors;
638 int maxc0, maxc1, maxc2;
639 int centerc0, centerc1, centerc2;
640 int i, x, ncolors;
641 JLONG minmaxdist, min_dist, max_dist, tdist;
642 JLONG mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
643
644 /* Compute true coordinates of update box's upper corner and center.
645 * Actually we compute the coordinates of the center of the upper-corner
646 * histogram cell, which are the upper bounds of the volume we care about.
647 * Note that since ">>" rounds down, the "center" values may be closer to
648 * min than to max; hence comparisons to them must be "<=", not "<".
649 */
650 maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
651 centerc0 = (minc0 + maxc0) >> 1;
652 maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
653 centerc1 = (minc1 + maxc1) >> 1;
654 maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
655 centerc2 = (minc2 + maxc2) >> 1;
656
657 /* For each color in colormap, find:
658 * 1. its minimum squared-distance to any point in the update box
659 * (zero if color is within update box);
660 * 2. its maximum squared-distance to any point in the update box.
661 * Both of these can be found by considering only the corners of the box.
662 * We save the minimum distance for each color in mindist[];
663 * only the smallest maximum distance is of interest.
664 */
665 minmaxdist = 0x7FFFFFFFL;
666
667 for (i = 0; i < numcolors; i++) {
668 /* We compute the squared-c0-distance term, then add in the other two. */
669 x = GETJSAMPLE(cinfo->colormap[0][i]);
670 if (x < minc0) {
671 tdist = (x - minc0) * C0_SCALE;
672 min_dist = tdist*tdist;
673 tdist = (x - maxc0) * C0_SCALE;
674 max_dist = tdist*tdist;
675 } else if (x > maxc0) {
676 tdist = (x - maxc0) * C0_SCALE;
677 min_dist = tdist*tdist;
678 tdist = (x - minc0) * C0_SCALE;
679 max_dist = tdist*tdist;
680 } else {
681 /* within cell range so no contribution to min_dist */
682 min_dist = 0;
683 if (x <= centerc0) {
684 tdist = (x - maxc0) * C0_SCALE;
685 max_dist = tdist*tdist;
686 } else {
687 tdist = (x - minc0) * C0_SCALE;
688 max_dist = tdist*tdist;
689 }
690 }
691
692 x = GETJSAMPLE(cinfo->colormap[1][i]);
693 if (x < minc1) {
694 tdist = (x - minc1) * C1_SCALE;
695 min_dist += tdist*tdist;
696 tdist = (x - maxc1) * C1_SCALE;
697 max_dist += tdist*tdist;
698 } else if (x > maxc1) {
699 tdist = (x - maxc1) * C1_SCALE;
700 min_dist += tdist*tdist;
701 tdist = (x - minc1) * C1_SCALE;
702 max_dist += tdist*tdist;
703 } else {
704 /* within cell range so no contribution to min_dist */
705 if (x <= centerc1) {
706 tdist = (x - maxc1) * C1_SCALE;
707 max_dist += tdist*tdist;
708 } else {
709 tdist = (x - minc1) * C1_SCALE;
710 max_dist += tdist*tdist;
711 }
712 }
713
714 x = GETJSAMPLE(cinfo->colormap[2][i]);
715 if (x < minc2) {
716 tdist = (x - minc2) * C2_SCALE;
717 min_dist += tdist*tdist;
718 tdist = (x - maxc2) * C2_SCALE;
719 max_dist += tdist*tdist;
720 } else if (x > maxc2) {
721 tdist = (x - maxc2) * C2_SCALE;
722 min_dist += tdist*tdist;
723 tdist = (x - minc2) * C2_SCALE;
724 max_dist += tdist*tdist;
725 } else {
726 /* within cell range so no contribution to min_dist */
727 if (x <= centerc2) {
728 tdist = (x - maxc2) * C2_SCALE;
729 max_dist += tdist*tdist;
730 } else {
731 tdist = (x - minc2) * C2_SCALE;
732 max_dist += tdist*tdist;
733 }
734 }
735
736 mindist[i] = min_dist; /* save away the results */
737 if (max_dist < minmaxdist)
738 minmaxdist = max_dist;
739 }
740
741 /* Now we know that no cell in the update box is more than minmaxdist
742 * away from some colormap entry. Therefore, only colors that are
743 * within minmaxdist of some part of the box need be considered.
744 */
745 ncolors = 0;
746 for (i = 0; i < numcolors; i++) {
747 if (mindist[i] <= minmaxdist)
748 colorlist[ncolors++] = (JSAMPLE) i;
749 }
750 return ncolors;
751}
752
753
754LOCAL(void)
755find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
756 int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
757/* Find the closest colormap entry for each cell in the update box,
758 * given the list of candidate colors prepared by find_nearby_colors.
759 * Return the indexes of the closest entries in the bestcolor[] array.
760 * This routine uses Thomas' incremental distance calculation method to
761 * find the distance from a colormap entry to successive cells in the box.
762 */
763{
764 int ic0, ic1, ic2;
765 int i, icolor;
766 register JLONG *bptr; /* pointer into bestdist[] array */
767 JSAMPLE *cptr; /* pointer into bestcolor[] array */
768 JLONG dist0, dist1; /* initial distance values */
769 register JLONG dist2; /* current distance in inner loop */
770 JLONG xx0, xx1; /* distance increments */
771 register JLONG xx2;
772 JLONG inc0, inc1, inc2; /* initial values for increments */
773 /* This array holds the distance to the nearest-so-far color for each cell */
774 JLONG bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
775
776 /* Initialize best-distance for each cell of the update box */
777 bptr = bestdist;
778 for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
779 *bptr++ = 0x7FFFFFFFL;
780
781 /* For each color selected by find_nearby_colors,
782 * compute its distance to the center of each cell in the box.
783 * If that's less than best-so-far, update best distance and color number.
784 */
785
786 /* Nominal steps between cell centers ("x" in Thomas article) */
787#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
788#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
789#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
790
791 for (i = 0; i < numcolors; i++) {
792 icolor = GETJSAMPLE(colorlist[i]);
793 /* Compute (square of) distance from minc0/c1/c2 to this color */
794 inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
795 dist0 = inc0*inc0;
796 inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
797 dist0 += inc1*inc1;
798 inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
799 dist0 += inc2*inc2;
800 /* Form the initial difference increments */
801 inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
802 inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
803 inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
804 /* Now loop over all cells in box, updating distance per Thomas method */
805 bptr = bestdist;
806 cptr = bestcolor;
807 xx0 = inc0;
808 for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
809 dist1 = dist0;
810 xx1 = inc1;
811 for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
812 dist2 = dist1;
813 xx2 = inc2;
814 for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
815 if (dist2 < *bptr) {
816 *bptr = dist2;
817 *cptr = (JSAMPLE) icolor;
818 }
819 dist2 += xx2;
820 xx2 += 2 * STEP_C2 * STEP_C2;
821 bptr++;
822 cptr++;
823 }
824 dist1 += xx1;
825 xx1 += 2 * STEP_C1 * STEP_C1;
826 }
827 dist0 += xx0;
828 xx0 += 2 * STEP_C0 * STEP_C0;
829 }
830 }
831}
832
833
834LOCAL(void)
835fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
836/* Fill the inverse-colormap entries in the update box that contains */
837/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
838/* we can fill as many others as we wish.) */
839{
840 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
841 hist3d histogram = cquantize->histogram;
842 int minc0, minc1, minc2; /* lower left corner of update box */
843 int ic0, ic1, ic2;
844 register JSAMPLE *cptr; /* pointer into bestcolor[] array */
845 register histptr cachep; /* pointer into main cache array */
846 /* This array lists the candidate colormap indexes. */
847 JSAMPLE colorlist[MAXNUMCOLORS];
848 int numcolors; /* number of candidate colors */
849 /* This array holds the actually closest colormap index for each cell. */
850 JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
851
852 /* Convert cell coordinates to update box ID */
853 c0 >>= BOX_C0_LOG;
854 c1 >>= BOX_C1_LOG;
855 c2 >>= BOX_C2_LOG;
856
857 /* Compute true coordinates of update box's origin corner.
858 * Actually we compute the coordinates of the center of the corner
859 * histogram cell, which are the lower bounds of the volume we care about.
860 */
861 minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
862 minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
863 minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
864
865 /* Determine which colormap entries are close enough to be candidates
866 * for the nearest entry to some cell in the update box.
867 */
868 numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
869
870 /* Determine the actually nearest colors. */
871 find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
872 bestcolor);
873
874 /* Save the best color numbers (plus 1) in the main cache array */
875 c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
876 c1 <<= BOX_C1_LOG;
877 c2 <<= BOX_C2_LOG;
878 cptr = bestcolor;
879 for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
880 for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
881 cachep = & histogram[c0+ic0][c1+ic1][c2];
882 for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
883 *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
884 }
885 }
886 }
887}
888
889
890/*
891 * Map some rows of pixels to the output colormapped representation.
892 */
893
894METHODDEF(void)
895pass2_no_dither (j_decompress_ptr cinfo,
896 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
897/* This version performs no dithering */
898{
899 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
900 hist3d histogram = cquantize->histogram;
901 register JSAMPROW inptr, outptr;
902 register histptr cachep;
903 register int c0, c1, c2;
904 int row;
905 JDIMENSION col;
906 JDIMENSION width = cinfo->output_width;
907
908 for (row = 0; row < num_rows; row++) {
909 inptr = input_buf[row];
910 outptr = output_buf[row];
911 for (col = width; col > 0; col--) {
912 /* get pixel value and index into the cache */
913 c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
914 c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
915 c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
916 cachep = & histogram[c0][c1][c2];
917 /* If we have not seen this color before, find nearest colormap entry */
918 /* and update the cache */
919 if (*cachep == 0)
920 fill_inverse_cmap(cinfo, c0,c1,c2);
921 /* Now emit the colormap index for this cell */
922 *outptr++ = (JSAMPLE) (*cachep - 1);
923 }
924 }
925}
926
927
928METHODDEF(void)
929pass2_fs_dither (j_decompress_ptr cinfo,
930 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
931/* This version performs Floyd-Steinberg dithering */
932{
933 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
934 hist3d histogram = cquantize->histogram;
935 register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
936 LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
937 LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
938 register FSERRPTR errorptr; /* => fserrors[] at column before current */
939 JSAMPROW inptr; /* => current input pixel */
940 JSAMPROW outptr; /* => current output pixel */
941 histptr cachep;
942 int dir; /* +1 or -1 depending on direction */
943 int dir3; /* 3*dir, for advancing inptr & errorptr */
944 int row;
945 JDIMENSION col;
946 JDIMENSION width = cinfo->output_width;
947 JSAMPLE *range_limit = cinfo->sample_range_limit;
948 int *error_limit = cquantize->error_limiter;
949 JSAMPROW colormap0 = cinfo->colormap[0];
950 JSAMPROW colormap1 = cinfo->colormap[1];
951 JSAMPROW colormap2 = cinfo->colormap[2];
952 SHIFT_TEMPS
953
954 for (row = 0; row < num_rows; row++) {
955 inptr = input_buf[row];
956 outptr = output_buf[row];
957 if (cquantize->on_odd_row) {
958 /* work right to left in this row */
959 inptr += (width-1) * 3; /* so point to rightmost pixel */
960 outptr += width-1;
961 dir = -1;
962 dir3 = -3;
963 errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
964 cquantize->on_odd_row = FALSE; /* flip for next time */
965 } else {
966 /* work left to right in this row */
967 dir = 1;
968 dir3 = 3;
969 errorptr = cquantize->fserrors; /* => entry before first real column */
970 cquantize->on_odd_row = TRUE; /* flip for next time */
971 }
972 /* Preset error values: no error propagated to first pixel from left */
973 cur0 = cur1 = cur2 = 0;
974 /* and no error propagated to row below yet */
975 belowerr0 = belowerr1 = belowerr2 = 0;
976 bpreverr0 = bpreverr1 = bpreverr2 = 0;
977
978 for (col = width; col > 0; col--) {
979 /* curN holds the error propagated from the previous pixel on the
980 * current line. Add the error propagated from the previous line
981 * to form the complete error correction term for this pixel, and
982 * round the error term (which is expressed * 16) to an integer.
983 * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
984 * for either sign of the error value.
985 * Note: errorptr points to *previous* column's array entry.
986 */
987 cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
988 cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
989 cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
990 /* Limit the error using transfer function set by init_error_limit.
991 * See comments with init_error_limit for rationale.
992 */
993 cur0 = error_limit[cur0];
994 cur1 = error_limit[cur1];
995 cur2 = error_limit[cur2];
996 /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
997 * The maximum error is +- MAXJSAMPLE (or less with error limiting);
998 * this sets the required size of the range_limit array.
999 */
1000 cur0 += GETJSAMPLE(inptr[0]);
1001 cur1 += GETJSAMPLE(inptr[1]);
1002 cur2 += GETJSAMPLE(inptr[2]);
1003 cur0 = GETJSAMPLE(range_limit[cur0]);
1004 cur1 = GETJSAMPLE(range_limit[cur1]);
1005 cur2 = GETJSAMPLE(range_limit[cur2]);
1006 /* Index into the cache with adjusted pixel value */
1007 cachep = & 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,cur2>>C2_SHIFT);
1012 /* Now emit the colormap index for this cell */
1013 { register int pixcode = *cachep - 1;
1014 *outptr = (JSAMPLE) pixcode;
1015 /* Compute representation error for this pixel */
1016 cur0 -= GETJSAMPLE(colormap0[pixcode]);
1017 cur1 -= GETJSAMPLE(colormap1[pixcode]);
1018 cur2 -= GETJSAMPLE(colormap2[pixcode]);
1019 }
1020 /* Compute error fractions to be propagated to adjacent pixels.
1021 * Add these into the running sums, and simultaneously shift the
1022 * next-line error sums left by 1 column.
1023 */
1024 { register LOCFSERROR bnexterr;
1025
1026 bnexterr = cur0; /* Process component 0 */
1027 errorptr[0] = (FSERROR) (bpreverr0 + cur0 * 3);
1028 bpreverr0 = belowerr0 + cur0 * 5;
1029 belowerr0 = bnexterr;
1030 cur0 *= 7;
1031 bnexterr = cur1; /* Process component 1 */
1032 errorptr[1] = (FSERROR) (bpreverr1 + cur1 * 3);
1033 bpreverr1 = belowerr1 + cur1 * 5;
1034 belowerr1 = bnexterr;
1035 cur1 *= 7;
1036 bnexterr = cur2; /* Process component 2 */
1037 errorptr[2] = (FSERROR) (bpreverr2 + cur2 * 3);
1038 bpreverr2 = belowerr2 + cur2 * 5;
1039 belowerr2 = bnexterr;
1040 cur2 *= 7;
1041 }
1042 /* At this point curN contains the 7/16 error value to be propagated
1043 * to the next pixel on the current line, and all the errors for the
1044 * next line have been shifted over. We are therefore ready to move on.
1045 */
1046 inptr += dir3; /* Advance pixel pointers to next column */
1047 outptr += dir;
1048 errorptr += dir3; /* advance errorptr to current column */
1049 }
1050 /* Post-loop cleanup: we must unload the final error values into the
1051 * final fserrors[] entry. Note we need not unload belowerrN because
1052 * it is for the dummy column before or after the actual array.
1053 */
1054 errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
1055 errorptr[1] = (FSERROR) bpreverr1;
1056 errorptr[2] = (FSERROR) bpreverr2;
1057 }
1058}
1059
1060
1061/*
1062 * Initialize the error-limiting transfer function (lookup table).
1063 * The raw F-S error computation can potentially compute error values of up to
1064 * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
1065 * much less, otherwise obviously wrong pixels will be created. (Typical
1066 * effects include weird fringes at color-area boundaries, isolated bright
1067 * pixels in a dark area, etc.) The standard advice for avoiding this problem
1068 * is to ensure that the "corners" of the color cube are allocated as output
1069 * colors; then repeated errors in the same direction cannot cause cascading
1070 * error buildup. However, that only prevents the error from getting
1071 * completely out of hand; Aaron Giles reports that error limiting improves
1072 * the results even with corner colors allocated.
1073 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1074 * well, but the smoother transfer function used below is even better. Thanks
1075 * to Aaron Giles for this idea.
1076 */
1077
1078LOCAL(void)
1079init_error_limit (j_decompress_ptr cinfo)
1080/* Allocate and fill in the error_limiter table */
1081{
1082 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1083 int *table;
1084 int in, out;
1085
1086 table = (int *) (*cinfo->mem->alloc_small)
1087 ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * sizeof(int));
1088 table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1089 cquantize->error_limiter = table;
1090
1091#define STEPSIZE ((MAXJSAMPLE+1)/16)
1092 /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1093 out = 0;
1094 for (in = 0; in < STEPSIZE; in++, out++) {
1095 table[in] = out; table[-in] = -out;
1096 }
1097 /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1098 for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
1099 table[in] = out; table[-in] = -out;
1100 }
1101 /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1102 for (; in <= MAXJSAMPLE; in++) {
1103 table[in] = out; table[-in] = -out;
1104 }
1105#undef STEPSIZE
1106}
1107
1108
1109/*
1110 * Finish up at the end of each pass.
1111 */
1112
1113METHODDEF(void)
1114finish_pass1 (j_decompress_ptr cinfo)
1115{
1116 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1117
1118 /* Select the representative colors and fill in cinfo->colormap */
1119 cinfo->colormap = cquantize->sv_colormap;
1120 select_colors(cinfo, cquantize->desired);
1121 /* Force next pass to zero the color index table */
1122 cquantize->needs_zeroed = TRUE;
1123}
1124
1125
1126METHODDEF(void)
1127finish_pass2 (j_decompress_ptr cinfo)
1128{
1129 /* no work */
1130}
1131
1132
1133/*
1134 * Initialize for each processing pass.
1135 */
1136
1137METHODDEF(void)
1138start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
1139{
1140 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1141 hist3d histogram = cquantize->histogram;
1142 int i;
1143
1144 /* Only F-S dithering or no dithering is supported. */
1145 /* If user asks for ordered dither, give him F-S. */
1146 if (cinfo->dither_mode != JDITHER_NONE)
1147 cinfo->dither_mode = JDITHER_FS;
1148
1149 if (is_pre_scan) {
1150 /* Set up method pointers */
1151 cquantize->pub.color_quantize = prescan_quantize;
1152 cquantize->pub.finish_pass = finish_pass1;
1153 cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1154 } else {
1155 /* Set up method pointers */
1156 if (cinfo->dither_mode == JDITHER_FS)
1157 cquantize->pub.color_quantize = pass2_fs_dither;
1158 else
1159 cquantize->pub.color_quantize = pass2_no_dither;
1160 cquantize->pub.finish_pass = finish_pass2;
1161
1162 /* Make sure color count is acceptable */
1163 i = cinfo->actual_number_of_colors;
1164 if (i < 1)
1165 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1166 if (i > MAXNUMCOLORS)
1167 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1168
1169 if (cinfo->dither_mode == JDITHER_FS) {
1170 size_t arraysize = (size_t) ((cinfo->output_width + 2) *
1171 (3 * sizeof(FSERROR)));
1172 /* Allocate Floyd-Steinberg workspace if we didn't already. */
1173 if (cquantize->fserrors == NULL)
1174 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1175 ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
1176 /* Initialize the propagated errors to zero. */
1177 jzero_far((void *) cquantize->fserrors, arraysize);
1178 /* Make the error-limit table if we didn't already. */
1179 if (cquantize->error_limiter == NULL)
1180 init_error_limit(cinfo);
1181 cquantize->on_odd_row = FALSE;
1182 }
1183
1184 }
1185 /* Zero the histogram or inverse color map, if necessary */
1186 if (cquantize->needs_zeroed) {
1187 for (i = 0; i < HIST_C0_ELEMS; i++) {
1188 jzero_far((void *) histogram[i],
1189 HIST_C1_ELEMS*HIST_C2_ELEMS * sizeof(histcell));
1190 }
1191 cquantize->needs_zeroed = FALSE;
1192 }
1193}
1194
1195
1196/*
1197 * Switch to a new external colormap between output passes.
1198 */
1199
1200METHODDEF(void)
1201new_color_map_2_quant (j_decompress_ptr cinfo)
1202{
1203 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1204
1205 /* Reset the inverse color map */
1206 cquantize->needs_zeroed = TRUE;
1207}
1208
1209
1210/*
1211 * Module initialization routine for 2-pass color quantization.
1212 */
1213
1214GLOBAL(void)
1215jinit_2pass_quantizer (j_decompress_ptr cinfo)
1216{
1217 my_cquantize_ptr cquantize;
1218 int i;
1219
1220 cquantize = (my_cquantize_ptr)
1221 (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
1222 sizeof(my_cquantizer));
1223 cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
1224 cquantize->pub.start_pass = start_pass_2_quant;
1225 cquantize->pub.new_color_map = new_color_map_2_quant;
1226 cquantize->fserrors = NULL; /* flag optional arrays not allocated */
1227 cquantize->error_limiter = NULL;
1228
1229 /* Make sure jdmaster didn't give me a case I can't handle */
1230 if (cinfo->out_color_components != 3)
1231 ERREXIT(cinfo, JERR_NOTIMPL);
1232
1233 /* Allocate the histogram/inverse colormap storage */
1234 cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
1235 ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * sizeof(hist2d));
1236 for (i = 0; i < HIST_C0_ELEMS; i++) {
1237 cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
1238 ((j_common_ptr) cinfo, JPOOL_IMAGE,
1239 HIST_C1_ELEMS*HIST_C2_ELEMS * sizeof(histcell));
1240 }
1241 cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1242
1243 /* Allocate storage for the completed colormap, if required.
1244 * We do this now since it may affect the memory manager's space
1245 * calculations.
1246 */
1247 if (cinfo->enable_2pass_quant) {
1248 /* Make sure color count is acceptable */
1249 int desired = cinfo->desired_number_of_colors;
1250 /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1251 if (desired < 8)
1252 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1253 /* Make sure colormap indexes can be represented by JSAMPLEs */
1254 if (desired > MAXNUMCOLORS)
1255 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1256 cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1257 ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
1258 cquantize->desired = desired;
1259 } else
1260 cquantize->sv_colormap = NULL;
1261
1262 /* Only F-S dithering or no dithering is supported. */
1263 /* If user asks for ordered dither, give him F-S. */
1264 if (cinfo->dither_mode != JDITHER_NONE)
1265 cinfo->dither_mode = JDITHER_FS;
1266
1267 /* Allocate Floyd-Steinberg workspace if necessary.
1268 * This isn't really needed until pass 2, but again it may affect the memory
1269 * manager's space calculations. Although we will cope with a later change
1270 * in dither_mode, we do not promise to honor max_memory_to_use if
1271 * dither_mode changes.
1272 */
1273 if (cinfo->dither_mode == JDITHER_FS) {
1274 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1275 ((j_common_ptr) cinfo, JPOOL_IMAGE,
1276 (size_t) ((cinfo->output_width + 2) * (3 * sizeof(FSERROR))));
1277 /* Might as well create the error-limiting table too. */
1278 init_error_limit(cinfo);
1279 }
1280}
1281
1282#endif /* QUANT_2PASS_SUPPORTED */
1283