/[pcsx2_0.9.7]/trunk/3rdparty/libjpeg/jquant2.c
ViewVC logotype

Contents of /trunk/3rdparty/libjpeg/jquant2.c

Parent Directory Parent Directory | Revision Log Revision Log


Revision 10 - (show annotations) (download)
Mon Sep 6 11:40:06 2010 UTC (9 years, 10 months ago) by william
File MIME type: text/plain
File size: 49714 byte(s)
exported r3113 from ./upstream/trunk
1 /*
2 * jquant2.c
3 *
4 * Copyright (C) 1991-1996, Thomas G. Lane.
5 * This file is part of the Independent JPEG Group's software.
6 * For conditions of distribution and use, see the accompanying README file.
7 *
8 * This file contains 2-pass color quantization (color mapping) routines.
9 * These routines provide selection of a custom color map for an image,
10 * followed by mapping of the image to that color map, with optional
11 * Floyd-Steinberg dithering.
12 * It is also possible to use just the second pass to map to an arbitrary
13 * externally-given color map.
14 *
15 * Note: ordered dithering is not supported, since there isn't any fast
16 * way to compute intercolor distances; it's unclear that ordered dither's
17 * fundamental assumptions even hold with an irregularly spaced color map.
18 */
19
20 #define JPEG_INTERNALS
21 #include "jinclude.h"
22 #include "jpeglib.h"
23
24 #ifdef QUANT_2PASS_SUPPORTED
25
26
27 /*
28 * This module implements the well-known Heckbert paradigm for color
29 * quantization. Most of the ideas used here can be traced back to
30 * Heckbert's seminal paper
31 * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
32 * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
33 *
34 * In the first pass over the image, we accumulate a histogram showing the
35 * usage count of each possible color. To keep the histogram to a reasonable
36 * size, we reduce the precision of the input; typical practice is to retain
37 * 5 or 6 bits per color, so that 8 or 4 different input values are counted
38 * in the same histogram cell.
39 *
40 * Next, the color-selection step begins with a box representing the whole
41 * color space, and repeatedly splits the "largest" remaining box until we
42 * have as many boxes as desired colors. Then the mean color in each
43 * remaining box becomes one of the possible output colors.
44 *
45 * The second pass over the image maps each input pixel to the closest output
46 * color (optionally after applying a Floyd-Steinberg dithering correction).
47 * This mapping is logically trivial, but making it go fast enough requires
48 * considerable care.
49 *
50 * Heckbert-style quantizers vary a good deal in their policies for choosing
51 * the "largest" box and deciding where to cut it. The particular policies
52 * used here have proved out well in experimental comparisons, but better ones
53 * may yet be found.
54 *
55 * In earlier versions of the IJG code, this module quantized in YCbCr color
56 * space, processing the raw upsampled data without a color conversion step.
57 * This allowed the color conversion math to be done only once per colormap
58 * entry, not once per pixel. However, that optimization precluded other
59 * useful optimizations (such as merging color conversion with upsampling)
60 * and it also interfered with desired capabilities such as quantizing to an
61 * externally-supplied colormap. We have therefore abandoned that approach.
62 * The present code works in the post-conversion color space, typically RGB.
63 *
64 * To improve the visual quality of the results, we actually work in scaled
65 * RGB space, giving G distances more weight than R, and R in turn more than
66 * B. To do everything in integer math, we must use integer scale factors.
67 * The 2/3/1 scale factors used here correspond loosely to the relative
68 * weights of the colors in the NTSC grayscale equation.
69 * If you want to use this code to quantize a non-RGB color space, you'll
70 * probably need to change these scale factors.
71 */
72
73 #define R_SCALE 2 /* scale R distances by this much */
74 #define G_SCALE 3 /* scale G distances by this much */
75 #define B_SCALE 1 /* and B by this much */
76
77 /* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
78 * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B
79 * and B,G,R orders. If you define some other weird order in jmorecfg.h,
80 * you'll get compile errors until you extend this logic. In that case
81 * you'll probably want to tweak the histogram sizes too.
82 */
83
84 #if RGB_RED == 0
85 #define C0_SCALE R_SCALE
86 #endif
87 #if RGB_BLUE == 0
88 #define C0_SCALE B_SCALE
89 #endif
90 #if RGB_GREEN == 1
91 #define C1_SCALE G_SCALE
92 #endif
93 #if RGB_RED == 2
94 #define C2_SCALE R_SCALE
95 #endif
96 #if RGB_BLUE == 2
97 #define C2_SCALE B_SCALE
98 #endif
99
100
101 /*
102 * First we have the histogram data structure and routines for creating it.
103 *
104 * The number of bits of precision can be adjusted by changing these symbols.
105 * We recommend keeping 6 bits for G and 5 each for R and B.
106 * If you have plenty of memory and cycles, 6 bits all around gives marginally
107 * better results; if you are short of memory, 5 bits all around will save
108 * some space but degrade the results.
109 * To maintain a fully accurate histogram, we'd need to allocate a "long"
110 * (preferably unsigned long) for each cell. In practice this is overkill;
111 * we can get by with 16 bits per cell. Few of the cell counts will overflow,
112 * and clamping those that do overflow to the maximum value will give close-
113 * enough results. This reduces the recommended histogram size from 256Kb
114 * to 128Kb, which is a useful savings on PC-class machines.
115 * (In the second pass the histogram space is re-used for pixel mapping data;
116 * in that capacity, each cell must be able to store zero to the number of
117 * desired colors. 16 bits/cell is plenty for that too.)
118 * Since the JPEG code is intended to run in small memory model on 80x86
119 * machines, we can't just allocate the histogram in one chunk. Instead
120 * of a true 3-D array, we use a row of pointers to 2-D arrays. Each
121 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
122 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
123 * on 80x86 machines, the pointer row is in near memory but the actual
124 * arrays are in far memory (same arrangement as we use for image arrays).
125 */
126
127 #define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
128
129 /* These will do the right thing for either R,G,B or B,G,R color order,
130 * but you may not like the results for other color orders.
131 */
132 #define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
133 #define HIST_C1_BITS 6 /* bits of precision in G histogram */
134 #define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
135
136 /* Number of elements along histogram axes. */
137 #define HIST_C0_ELEMS (1<<HIST_C0_BITS)
138 #define HIST_C1_ELEMS (1<<HIST_C1_BITS)
139 #define HIST_C2_ELEMS (1<<HIST_C2_BITS)
140
141 /* These are the amounts to shift an input value to get a histogram index. */
142 #define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS)
143 #define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS)
144 #define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS)
145
146
147 typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */
148
149 typedef histcell FAR * histptr; /* for pointers to histogram cells */
150
151 typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
152 typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */
153 typedef hist2d * hist3d; /* type for top-level pointer */
154
155
156 /* Declarations for Floyd-Steinberg dithering.
157 *
158 * Errors are accumulated into the array fserrors[], at a resolution of
159 * 1/16th of a pixel count. The error at a given pixel is propagated
160 * to its not-yet-processed neighbors using the standard F-S fractions,
161 * ... (here) 7/16
162 * 3/16 5/16 1/16
163 * We work left-to-right on even rows, right-to-left on odd rows.
164 *
165 * We can get away with a single array (holding one row's worth of errors)
166 * by using it to store the current row's errors at pixel columns not yet
167 * processed, but the next row's errors at columns already processed. We
168 * need only a few extra variables to hold the errors immediately around the
169 * current column. (If we are lucky, those variables are in registers, but
170 * even if not, they're probably cheaper to access than array elements are.)
171 *
172 * The fserrors[] array has (#columns + 2) entries; the extra entry at
173 * each end saves us from special-casing the first and last pixels.
174 * Each entry is three values long, one value for each color component.
175 *
176 * Note: on a wide image, we might not have enough room in a PC's near data
177 * segment to hold the error array; so it is allocated with alloc_large.
178 */
179
180 #if BITS_IN_JSAMPLE == 8
181 typedef INT16 FSERROR; /* 16 bits should be enough */
182 typedef int LOCFSERROR; /* use 'int' for calculation temps */
183 #else
184 typedef INT32 FSERROR; /* may need more than 16 bits */
185 typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */
186 #endif
187
188 typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */
189
190
191 /* Private subobject */
192
193 typedef struct {
194 struct jpeg_color_quantizer pub; /* public fields */
195
196 /* Space for the eventually created colormap is stashed here */
197 JSAMPARRAY sv_colormap; /* colormap allocated at init time */
198 int desired; /* desired # of colors = size of colormap */
199
200 /* Variables for accumulating image statistics */
201 hist3d histogram; /* pointer to the histogram */
202
203 boolean needs_zeroed; /* TRUE if next pass must zero histogram */
204
205 /* Variables for Floyd-Steinberg dithering */
206 FSERRPTR fserrors; /* accumulated errors */
207 boolean on_odd_row; /* flag to remember which row we are on */
208 int * error_limiter; /* table for clamping the applied error */
209 } my_cquantizer;
210
211 typedef my_cquantizer * my_cquantize_ptr;
212
213
214 /*
215 * Prescan some rows of pixels.
216 * In this module the prescan simply updates the histogram, which has been
217 * initialized to zeroes by start_pass.
218 * An output_buf parameter is required by the method signature, but no data
219 * is actually output (in fact the buffer controller is probably passing a
220 * NULL pointer).
221 */
222
223 METHODDEF(void)
224 prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
225 JSAMPARRAY output_buf, int num_rows)
226 {
227 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
228 register JSAMPROW ptr;
229 register histptr histp;
230 register hist3d histogram = cquantize->histogram;
231 int row;
232 JDIMENSION col;
233 JDIMENSION width = cinfo->output_width;
234
235 for (row = 0; row < num_rows; row++) {
236 ptr = input_buf[row];
237 for (col = width; col > 0; col--) {
238 /* get pixel value and index into the histogram */
239 histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
240 [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
241 [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
242 /* increment, check for overflow and undo increment if so. */
243 if (++(*histp) <= 0)
244 (*histp)--;
245 ptr += 3;
246 }
247 }
248 }
249
250
251 /*
252 * Next we have the really interesting routines: selection of a colormap
253 * given the completed histogram.
254 * These routines work with a list of "boxes", each representing a rectangular
255 * subset of the input color space (to histogram precision).
256 */
257
258 typedef struct {
259 /* The bounds of the box (inclusive); expressed as histogram indexes */
260 int c0min, c0max;
261 int c1min, c1max;
262 int c2min, c2max;
263 /* The volume (actually 2-norm) of the box */
264 INT32 volume;
265 /* The number of nonzero histogram cells within this box */
266 long colorcount;
267 } box;
268
269 typedef box * boxptr;
270
271
272 LOCAL(boxptr)
273 find_biggest_color_pop (boxptr boxlist, int numboxes)
274 /* Find the splittable box with the largest color population */
275 /* Returns NULL if no splittable boxes remain */
276 {
277 register boxptr boxp;
278 register int i;
279 register long maxc = 0;
280 boxptr which = NULL;
281
282 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
283 if (boxp->colorcount > maxc && boxp->volume > 0) {
284 which = boxp;
285 maxc = boxp->colorcount;
286 }
287 }
288 return which;
289 }
290
291
292 LOCAL(boxptr)
293 find_biggest_volume (boxptr boxlist, int numboxes)
294 /* Find the splittable box with the largest (scaled) volume */
295 /* Returns NULL if no splittable boxes remain */
296 {
297 register boxptr boxp;
298 register int i;
299 register INT32 maxv = 0;
300 boxptr which = NULL;
301
302 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
303 if (boxp->volume > maxv) {
304 which = boxp;
305 maxv = boxp->volume;
306 }
307 }
308 return which;
309 }
310
311
312 LOCAL(void)
313 update_box (j_decompress_ptr cinfo, boxptr boxp)
314 /* Shrink the min/max bounds of a box to enclose only nonzero elements, */
315 /* and recompute its volume and population */
316 {
317 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
318 hist3d histogram = cquantize->histogram;
319 histptr histp;
320 int c0,c1,c2;
321 int c0min,c0max,c1min,c1max,c2min,c2max;
322 INT32 dist0,dist1,dist2;
323 long ccount;
324
325 c0min = boxp->c0min; c0max = boxp->c0max;
326 c1min = boxp->c1min; c1max = boxp->c1max;
327 c2min = boxp->c2min; c2max = boxp->c2max;
328
329 if (c0max > c0min)
330 for (c0 = c0min; c0 <= c0max; c0++)
331 for (c1 = c1min; c1 <= c1max; c1++) {
332 histp = & histogram[c0][c1][c2min];
333 for (c2 = c2min; c2 <= c2max; c2++)
334 if (*histp++ != 0) {
335 boxp->c0min = c0min = c0;
336 goto have_c0min;
337 }
338 }
339 have_c0min:
340 if (c0max > c0min)
341 for (c0 = c0max; c0 >= c0min; c0--)
342 for (c1 = c1min; c1 <= c1max; c1++) {
343 histp = & histogram[c0][c1][c2min];
344 for (c2 = c2min; c2 <= c2max; c2++)
345 if (*histp++ != 0) {
346 boxp->c0max = c0max = c0;
347 goto have_c0max;
348 }
349 }
350 have_c0max:
351 if (c1max > c1min)
352 for (c1 = c1min; c1 <= c1max; c1++)
353 for (c0 = c0min; c0 <= c0max; c0++) {
354 histp = & histogram[c0][c1][c2min];
355 for (c2 = c2min; c2 <= c2max; c2++)
356 if (*histp++ != 0) {
357 boxp->c1min = c1min = c1;
358 goto have_c1min;
359 }
360 }
361 have_c1min:
362 if (c1max > c1min)
363 for (c1 = c1max; c1 >= c1min; c1--)
364 for (c0 = c0min; c0 <= c0max; c0++) {
365 histp = & histogram[c0][c1][c2min];
366 for (c2 = c2min; c2 <= c2max; c2++)
367 if (*histp++ != 0) {
368 boxp->c1max = c1max = c1;
369 goto have_c1max;
370 }
371 }
372 have_c1max:
373 if (c2max > c2min)
374 for (c2 = c2min; c2 <= c2max; c2++)
375 for (c0 = c0min; c0 <= c0max; c0++) {
376 histp = & histogram[c0][c1min][c2];
377 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
378 if (*histp != 0) {
379 boxp->c2min = c2min = c2;
380 goto have_c2min;
381 }
382 }
383 have_c2min:
384 if (c2max > c2min)
385 for (c2 = c2max; c2 >= c2min; c2--)
386 for (c0 = c0min; c0 <= c0max; c0++) {
387 histp = & histogram[c0][c1min][c2];
388 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
389 if (*histp != 0) {
390 boxp->c2max = c2max = c2;
391 goto have_c2max;
392 }
393 }
394 have_c2max:
395
396 /* Update box volume.
397 * We use 2-norm rather than real volume here; this biases the method
398 * against making long narrow boxes, and it has the side benefit that
399 * a box is splittable iff norm > 0.
400 * Since the differences are expressed in histogram-cell units,
401 * we have to shift back to JSAMPLE units to get consistent distances;
402 * after which, we scale according to the selected distance scale factors.
403 */
404 dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
405 dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
406 dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
407 boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
408
409 /* Now scan remaining volume of box and compute population */
410 ccount = 0;
411 for (c0 = c0min; c0 <= c0max; c0++)
412 for (c1 = c1min; c1 <= c1max; c1++) {
413 histp = & histogram[c0][c1][c2min];
414 for (c2 = c2min; c2 <= c2max; c2++, histp++)
415 if (*histp != 0) {
416 ccount++;
417 }
418 }
419 boxp->colorcount = ccount;
420 }
421
422
423 LOCAL(int)
424 median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
425 int desired_colors)
426 /* Repeatedly select and split the largest box until we have enough boxes */
427 {
428 int n,lb;
429 int c0,c1,c2,cmax;
430 register boxptr b1,b2;
431
432 while (numboxes < desired_colors) {
433 /* Select box to split.
434 * Current algorithm: by population for first half, then by volume.
435 */
436 if (numboxes*2 <= desired_colors) {
437 b1 = find_biggest_color_pop(boxlist, numboxes);
438 } else {
439 b1 = find_biggest_volume(boxlist, numboxes);
440 }
441 if (b1 == NULL) /* no splittable boxes left! */
442 break;
443 b2 = &boxlist[numboxes]; /* where new box will go */
444 /* Copy the color bounds to the new box. */
445 b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
446 b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
447 /* Choose which axis to split the box on.
448 * Current algorithm: longest scaled axis.
449 * See notes in update_box about scaling distances.
450 */
451 c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
452 c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
453 c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
454 /* We want to break any ties in favor of green, then red, blue last.
455 * This code does the right thing for R,G,B or B,G,R color orders only.
456 */
457 #if RGB_RED == 0
458 cmax = c1; n = 1;
459 if (c0 > cmax) { cmax = c0; n = 0; }
460 if (c2 > cmax) { n = 2; }
461 #else
462 cmax = c1; n = 1;
463 if (c2 > cmax) { cmax = c2; n = 2; }
464 if (c0 > cmax) { n = 0; }
465 #endif
466 /* Choose split point along selected axis, and update box bounds.
467 * Current algorithm: split at halfway point.
468 * (Since the box has been shrunk to minimum volume,
469 * any split will produce two nonempty subboxes.)
470 * Note that lb value is max for lower box, so must be < old max.
471 */
472 switch (n) {
473 case 0:
474 lb = (b1->c0max + b1->c0min) / 2;
475 b1->c0max = lb;
476 b2->c0min = lb+1;
477 break;
478 case 1:
479 lb = (b1->c1max + b1->c1min) / 2;
480 b1->c1max = lb;
481 b2->c1min = lb+1;
482 break;
483 case 2:
484 lb = (b1->c2max + b1->c2min) / 2;
485 b1->c2max = lb;
486 b2->c2min = lb+1;
487 break;
488 }
489 /* Update stats for boxes */
490 update_box(cinfo, b1);
491 update_box(cinfo, b2);
492 numboxes++;
493 }
494 return numboxes;
495 }
496
497
498 LOCAL(void)
499 compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
500 /* Compute representative color for a box, put it in colormap[icolor] */
501 {
502 /* Current algorithm: mean weighted by pixels (not colors) */
503 /* Note it is important to get the rounding correct! */
504 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
505 hist3d histogram = cquantize->histogram;
506 histptr histp;
507 int c0,c1,c2;
508 int c0min,c0max,c1min,c1max,c2min,c2max;
509 long count;
510 long total = 0;
511 long c0total = 0;
512 long c1total = 0;
513 long c2total = 0;
514
515 c0min = boxp->c0min; c0max = boxp->c0max;
516 c1min = boxp->c1min; c1max = boxp->c1max;
517 c2min = boxp->c2min; c2max = boxp->c2max;
518
519 for (c0 = c0min; c0 <= c0max; c0++)
520 for (c1 = c1min; c1 <= c1max; c1++) {
521 histp = & histogram[c0][c1][c2min];
522 for (c2 = c2min; c2 <= c2max; c2++) {
523 if ((count = *histp++) != 0) {
524 total += count;
525 c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
526 c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
527 c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
528 }
529 }
530 }
531
532 cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
533 cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
534 cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
535 }
536
537
538 LOCAL(void)
539 select_colors (j_decompress_ptr cinfo, int desired_colors)
540 /* Master routine for color selection */
541 {
542 boxptr boxlist;
543 int numboxes;
544 int i;
545
546 /* Allocate workspace for box list */
547 boxlist = (boxptr) (*cinfo->mem->alloc_small)
548 ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
549 /* Initialize one box containing whole space */
550 numboxes = 1;
551 boxlist[0].c0min = 0;
552 boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
553 boxlist[0].c1min = 0;
554 boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
555 boxlist[0].c2min = 0;
556 boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
557 /* Shrink it to actually-used volume and set its statistics */
558 update_box(cinfo, & boxlist[0]);
559 /* Perform median-cut to produce final box list */
560 numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
561 /* Compute the representative color for each box, fill colormap */
562 for (i = 0; i < numboxes; i++)
563 compute_color(cinfo, & boxlist[i], i);
564 cinfo->actual_number_of_colors = numboxes;
565 TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
566 }
567
568
569 /*
570 * These routines are concerned with the time-critical task of mapping input
571 * colors to the nearest color in the selected colormap.
572 *
573 * We re-use the histogram space as an "inverse color map", essentially a
574 * cache for the results of nearest-color searches. All colors within a
575 * histogram cell will be mapped to the same colormap entry, namely the one
576 * closest to the cell's center. This may not be quite the closest entry to
577 * the actual input color, but it's almost as good. A zero in the cache
578 * indicates we haven't found the nearest color for that cell yet; the array
579 * is cleared to zeroes before starting the mapping pass. When we find the
580 * nearest color for a cell, its colormap index plus one is recorded in the
581 * cache for future use. The pass2 scanning routines call fill_inverse_cmap
582 * when they need to use an unfilled entry in the cache.
583 *
584 * Our method of efficiently finding nearest colors is based on the "locally
585 * sorted search" idea described by Heckbert and on the incremental distance
586 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
587 * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
588 * the distances from a given colormap entry to each cell of the histogram can
589 * be computed quickly using an incremental method: the differences between
590 * distances to adjacent cells themselves differ by a constant. This allows a
591 * fairly fast implementation of the "brute force" approach of computing the
592 * distance from every colormap entry to every histogram cell. Unfortunately,
593 * it needs a work array to hold the best-distance-so-far for each histogram
594 * cell (because the inner loop has to be over cells, not colormap entries).
595 * The work array elements have to be INT32s, so the work array would need
596 * 256Kb at our recommended precision. This is not feasible in DOS machines.
597 *
598 * To get around these problems, we apply Thomas' method to compute the
599 * nearest colors for only the cells within a small subbox of the histogram.
600 * The work array need be only as big as the subbox, so the memory usage
601 * problem is solved. Furthermore, we need not fill subboxes that are never
602 * referenced in pass2; many images use only part of the color gamut, so a
603 * fair amount of work is saved. An additional advantage of this
604 * approach is that we can apply Heckbert's locality criterion to quickly
605 * eliminate colormap entries that are far away from the subbox; typically
606 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
607 * and we need not compute their distances to individual cells in the subbox.
608 * The speed of this approach is heavily influenced by the subbox size: too
609 * small means too much overhead, too big loses because Heckbert's criterion
610 * can't eliminate as many colormap entries. Empirically the best subbox
611 * size seems to be about 1/512th of the histogram (1/8th in each direction).
612 *
613 * Thomas' article also describes a refined method which is asymptotically
614 * faster than the brute-force method, but it is also far more complex and
615 * cannot efficiently be applied to small subboxes. It is therefore not
616 * useful for programs intended to be portable to DOS machines. On machines
617 * with plenty of memory, filling the whole histogram in one shot with Thomas'
618 * refined method might be faster than the present code --- but then again,
619 * it might not be any faster, and it's certainly more complicated.
620 */
621
622
623 /* log2(histogram cells in update box) for each axis; this can be adjusted */
624 #define BOX_C0_LOG (HIST_C0_BITS-3)
625 #define BOX_C1_LOG (HIST_C1_BITS-3)
626 #define BOX_C2_LOG (HIST_C2_BITS-3)
627
628 #define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */
629 #define BOX_C1_ELEMS (1<<BOX_C1_LOG)
630 #define BOX_C2_ELEMS (1<<BOX_C2_LOG)
631
632 #define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
633 #define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
634 #define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)
635
636
637 /*
638 * The next three routines implement inverse colormap filling. They could
639 * all be folded into one big routine, but splitting them up this way saves
640 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
641 * and may allow some compilers to produce better code by registerizing more
642 * inner-loop variables.
643 */
644
645 LOCAL(int)
646 find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
647 JSAMPLE colorlist[])
648 /* Locate the colormap entries close enough to an update box to be candidates
649 * for the nearest entry to some cell(s) in the update box. The update box
650 * is specified by the center coordinates of its first cell. The number of
651 * candidate colormap entries is returned, and their colormap indexes are
652 * placed in colorlist[].
653 * This routine uses Heckbert's "locally sorted search" criterion to select
654 * the colors that need further consideration.
655 */
656 {
657 int numcolors = cinfo->actual_number_of_colors;
658 int maxc0, maxc1, maxc2;
659 int centerc0, centerc1, centerc2;
660 int i, x, ncolors;
661 INT32 minmaxdist, min_dist, max_dist, tdist;
662 INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
663
664 /* Compute true coordinates of update box's upper corner and center.
665 * Actually we compute the coordinates of the center of the upper-corner
666 * histogram cell, which are the upper bounds of the volume we care about.
667 * Note that since ">>" rounds down, the "center" values may be closer to
668 * min than to max; hence comparisons to them must be "<=", not "<".
669 */
670 maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
671 centerc0 = (minc0 + maxc0) >> 1;
672 maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
673 centerc1 = (minc1 + maxc1) >> 1;
674 maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
675 centerc2 = (minc2 + maxc2) >> 1;
676
677 /* For each color in colormap, find:
678 * 1. its minimum squared-distance to any point in the update box
679 * (zero if color is within update box);
680 * 2. its maximum squared-distance to any point in the update box.
681 * Both of these can be found by considering only the corners of the box.
682 * We save the minimum distance for each color in mindist[];
683 * only the smallest maximum distance is of interest.
684 */
685 minmaxdist = 0x7FFFFFFFL;
686
687 for (i = 0; i < numcolors; i++) {
688 /* We compute the squared-c0-distance term, then add in the other two. */
689 x = GETJSAMPLE(cinfo->colormap[0][i]);
690 if (x < minc0) {
691 tdist = (x - minc0) * C0_SCALE;
692 min_dist = tdist*tdist;
693 tdist = (x - maxc0) * C0_SCALE;
694 max_dist = tdist*tdist;
695 } else if (x > maxc0) {
696 tdist = (x - maxc0) * C0_SCALE;
697 min_dist = tdist*tdist;
698 tdist = (x - minc0) * C0_SCALE;
699 max_dist = tdist*tdist;
700 } else {
701 /* within cell range so no contribution to min_dist */
702 min_dist = 0;
703 if (x <= centerc0) {
704 tdist = (x - maxc0) * C0_SCALE;
705 max_dist = tdist*tdist;
706 } else {
707 tdist = (x - minc0) * C0_SCALE;
708 max_dist = tdist*tdist;
709 }
710 }
711
712 x = GETJSAMPLE(cinfo->colormap[1][i]);
713 if (x < minc1) {
714 tdist = (x - minc1) * C1_SCALE;
715 min_dist += tdist*tdist;
716 tdist = (x - maxc1) * C1_SCALE;
717 max_dist += tdist*tdist;
718 } else if (x > maxc1) {
719 tdist = (x - maxc1) * C1_SCALE;
720 min_dist += tdist*tdist;
721 tdist = (x - minc1) * C1_SCALE;
722 max_dist += tdist*tdist;
723 } else {
724 /* within cell range so no contribution to min_dist */
725 if (x <= centerc1) {
726 tdist = (x - maxc1) * C1_SCALE;
727 max_dist += tdist*tdist;
728 } else {
729 tdist = (x - minc1) * C1_SCALE;
730 max_dist += tdist*tdist;
731 }
732 }
733
734 x = GETJSAMPLE(cinfo->colormap[2][i]);
735 if (x < minc2) {
736 tdist = (x - minc2) * C2_SCALE;
737 min_dist += tdist*tdist;
738 tdist = (x - maxc2) * C2_SCALE;
739 max_dist += tdist*tdist;
740 } else if (x > maxc2) {
741 tdist = (x - maxc2) * C2_SCALE;
742 min_dist += tdist*tdist;
743 tdist = (x - minc2) * C2_SCALE;
744 max_dist += tdist*tdist;
745 } else {
746 /* within cell range so no contribution to min_dist */
747 if (x <= centerc2) {
748 tdist = (x - maxc2) * C2_SCALE;
749 max_dist += tdist*tdist;
750 } else {
751 tdist = (x - minc2) * C2_SCALE;
752 max_dist += tdist*tdist;
753 }
754 }
755
756 mindist[i] = min_dist; /* save away the results */
757 if (max_dist < minmaxdist)
758 minmaxdist = max_dist;
759 }
760
761 /* Now we know that no cell in the update box is more than minmaxdist
762 * away from some colormap entry. Therefore, only colors that are
763 * within minmaxdist of some part of the box need be considered.
764 */
765 ncolors = 0;
766 for (i = 0; i < numcolors; i++) {
767 if (mindist[i] <= minmaxdist)
768 colorlist[ncolors++] = (JSAMPLE) i;
769 }
770 return ncolors;
771 }
772
773
774 LOCAL(void)
775 find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
776 int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
777 /* Find the closest colormap entry for each cell in the update box,
778 * given the list of candidate colors prepared by find_nearby_colors.
779 * Return the indexes of the closest entries in the bestcolor[] array.
780 * This routine uses Thomas' incremental distance calculation method to
781 * find the distance from a colormap entry to successive cells in the box.
782 */
783 {
784 int ic0, ic1, ic2;
785 int i, icolor;
786 register INT32 * bptr; /* pointer into bestdist[] array */
787 JSAMPLE * cptr; /* pointer into bestcolor[] array */
788 INT32 dist0, dist1; /* initial distance values */
789 register INT32 dist2; /* current distance in inner loop */
790 INT32 xx0, xx1; /* distance increments */
791 register INT32 xx2;
792 INT32 inc0, inc1, inc2; /* initial values for increments */
793 /* This array holds the distance to the nearest-so-far color for each cell */
794 INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
795
796 /* Initialize best-distance for each cell of the update box */
797 bptr = bestdist;
798 for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
799 *bptr++ = 0x7FFFFFFFL;
800
801 /* For each color selected by find_nearby_colors,
802 * compute its distance to the center of each cell in the box.
803 * If that's less than best-so-far, update best distance and color number.
804 */
805
806 /* Nominal steps between cell centers ("x" in Thomas article) */
807 #define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
808 #define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
809 #define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
810
811 for (i = 0; i < numcolors; i++) {
812 icolor = GETJSAMPLE(colorlist[i]);
813 /* Compute (square of) distance from minc0/c1/c2 to this color */
814 inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
815 dist0 = inc0*inc0;
816 inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
817 dist0 += inc1*inc1;
818 inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
819 dist0 += inc2*inc2;
820 /* Form the initial difference increments */
821 inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
822 inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
823 inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
824 /* Now loop over all cells in box, updating distance per Thomas method */
825 bptr = bestdist;
826 cptr = bestcolor;
827 xx0 = inc0;
828 for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
829 dist1 = dist0;
830 xx1 = inc1;
831 for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
832 dist2 = dist1;
833 xx2 = inc2;
834 for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
835 if (dist2 < *bptr) {
836 *bptr = dist2;
837 *cptr = (JSAMPLE) icolor;
838 }
839 dist2 += xx2;
840 xx2 += 2 * STEP_C2 * STEP_C2;
841 bptr++;
842 cptr++;
843 }
844 dist1 += xx1;
845 xx1 += 2 * STEP_C1 * STEP_C1;
846 }
847 dist0 += xx0;
848 xx0 += 2 * STEP_C0 * STEP_C0;
849 }
850 }
851 }
852
853
854 LOCAL(void)
855 fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
856 /* Fill the inverse-colormap entries in the update box that contains */
857 /* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
858 /* we can fill as many others as we wish.) */
859 {
860 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
861 hist3d histogram = cquantize->histogram;
862 int minc0, minc1, minc2; /* lower left corner of update box */
863 int ic0, ic1, ic2;
864 register JSAMPLE * cptr; /* pointer into bestcolor[] array */
865 register histptr cachep; /* pointer into main cache array */
866 /* This array lists the candidate colormap indexes. */
867 JSAMPLE colorlist[MAXNUMCOLORS];
868 int numcolors; /* number of candidate colors */
869 /* This array holds the actually closest colormap index for each cell. */
870 JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
871
872 /* Convert cell coordinates to update box ID */
873 c0 >>= BOX_C0_LOG;
874 c1 >>= BOX_C1_LOG;
875 c2 >>= BOX_C2_LOG;
876
877 /* Compute true coordinates of update box's origin corner.
878 * Actually we compute the coordinates of the center of the corner
879 * histogram cell, which are the lower bounds of the volume we care about.
880 */
881 minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
882 minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
883 minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
884
885 /* Determine which colormap entries are close enough to be candidates
886 * for the nearest entry to some cell in the update box.
887 */
888 numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
889
890 /* Determine the actually nearest colors. */
891 find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
892 bestcolor);
893
894 /* Save the best color numbers (plus 1) in the main cache array */
895 c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
896 c1 <<= BOX_C1_LOG;
897 c2 <<= BOX_C2_LOG;
898 cptr = bestcolor;
899 for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
900 for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
901 cachep = & histogram[c0+ic0][c1+ic1][c2];
902 for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
903 *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
904 }
905 }
906 }
907 }
908
909
910 /*
911 * Map some rows of pixels to the output colormapped representation.
912 */
913
914 METHODDEF(void)
915 pass2_no_dither (j_decompress_ptr cinfo,
916 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
917 /* This version performs no dithering */
918 {
919 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
920 hist3d histogram = cquantize->histogram;
921 register JSAMPROW inptr, outptr;
922 register histptr cachep;
923 register int c0, c1, c2;
924 int row;
925 JDIMENSION col;
926 JDIMENSION width = cinfo->output_width;
927
928 for (row = 0; row < num_rows; row++) {
929 inptr = input_buf[row];
930 outptr = output_buf[row];
931 for (col = width; col > 0; col--) {
932 /* get pixel value and index into the cache */
933 c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
934 c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
935 c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
936 cachep = & histogram[c0][c1][c2];
937 /* If we have not seen this color before, find nearest colormap entry */
938 /* and update the cache */
939 if (*cachep == 0)
940 fill_inverse_cmap(cinfo, c0,c1,c2);
941 /* Now emit the colormap index for this cell */
942 *outptr++ = (JSAMPLE) (*cachep - 1);
943 }
944 }
945 }
946
947
948 METHODDEF(void)
949 pass2_fs_dither (j_decompress_ptr cinfo,
950 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
951 /* This version performs Floyd-Steinberg dithering */
952 {
953 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
954 hist3d histogram = cquantize->histogram;
955 register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
956 LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
957 LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
958 register FSERRPTR errorptr; /* => fserrors[] at column before current */
959 JSAMPROW inptr; /* => current input pixel */
960 JSAMPROW outptr; /* => current output pixel */
961 histptr cachep;
962 int dir; /* +1 or -1 depending on direction */
963 int dir3; /* 3*dir, for advancing inptr & errorptr */
964 int row;
965 JDIMENSION col;
966 JDIMENSION width = cinfo->output_width;
967 JSAMPLE *range_limit = cinfo->sample_range_limit;
968 int *error_limit = cquantize->error_limiter;
969 JSAMPROW colormap0 = cinfo->colormap[0];
970 JSAMPROW colormap1 = cinfo->colormap[1];
971 JSAMPROW colormap2 = cinfo->colormap[2];
972 SHIFT_TEMPS
973
974 for (row = 0; row < num_rows; row++) {
975 inptr = input_buf[row];
976 outptr = output_buf[row];
977 if (cquantize->on_odd_row) {
978 /* work right to left in this row */
979 inptr += (width-1) * 3; /* so point to rightmost pixel */
980 outptr += width-1;
981 dir = -1;
982 dir3 = -3;
983 errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
984 cquantize->on_odd_row = FALSE; /* flip for next time */
985 } else {
986 /* work left to right in this row */
987 dir = 1;
988 dir3 = 3;
989 errorptr = cquantize->fserrors; /* => entry before first real column */
990 cquantize->on_odd_row = TRUE; /* flip for next time */
991 }
992 /* Preset error values: no error propagated to first pixel from left */
993 cur0 = cur1 = cur2 = 0;
994 /* and no error propagated to row below yet */
995 belowerr0 = belowerr1 = belowerr2 = 0;
996 bpreverr0 = bpreverr1 = bpreverr2 = 0;
997
998 for (col = width; col > 0; col--) {
999 /* curN holds the error propagated from the previous pixel on the
1000 * current line. Add the error propagated from the previous line
1001 * to form the complete error correction term for this pixel, and
1002 * round the error term (which is expressed * 16) to an integer.
1003 * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
1004 * for either sign of the error value.
1005 * Note: errorptr points to *previous* column's array entry.
1006 */
1007 cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
1008 cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
1009 cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
1010 /* Limit the error using transfer function set by init_error_limit.
1011 * See comments with init_error_limit for rationale.
1012 */
1013 cur0 = error_limit[cur0];
1014 cur1 = error_limit[cur1];
1015 cur2 = error_limit[cur2];
1016 /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
1017 * The maximum error is +- MAXJSAMPLE (or less with error limiting);
1018 * this sets the required size of the range_limit array.
1019 */
1020 cur0 += GETJSAMPLE(inptr[0]);
1021 cur1 += GETJSAMPLE(inptr[1]);
1022 cur2 += GETJSAMPLE(inptr[2]);
1023 cur0 = GETJSAMPLE(range_limit[cur0]);
1024 cur1 = GETJSAMPLE(range_limit[cur1]);
1025 cur2 = GETJSAMPLE(range_limit[cur2]);
1026 /* Index into the cache with adjusted pixel value */
1027 cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
1028 /* If we have not seen this color before, find nearest colormap */
1029 /* entry and update the cache */
1030 if (*cachep == 0)
1031 fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
1032 /* Now emit the colormap index for this cell */
1033 { register int pixcode = *cachep - 1;
1034 *outptr = (JSAMPLE) pixcode;
1035 /* Compute representation error for this pixel */
1036 cur0 -= GETJSAMPLE(colormap0[pixcode]);
1037 cur1 -= GETJSAMPLE(colormap1[pixcode]);
1038 cur2 -= GETJSAMPLE(colormap2[pixcode]);
1039 }
1040 /* Compute error fractions to be propagated to adjacent pixels.
1041 * Add these into the running sums, and simultaneously shift the
1042 * next-line error sums left by 1 column.
1043 */
1044 { register LOCFSERROR bnexterr, delta;
1045
1046 bnexterr = cur0; /* Process component 0 */
1047 delta = cur0 * 2;
1048 cur0 += delta; /* form error * 3 */
1049 errorptr[0] = (FSERROR) (bpreverr0 + cur0);
1050 cur0 += delta; /* form error * 5 */
1051 bpreverr0 = belowerr0 + cur0;
1052 belowerr0 = bnexterr;
1053 cur0 += delta; /* form error * 7 */
1054 bnexterr = cur1; /* Process component 1 */
1055 delta = cur1 * 2;
1056 cur1 += delta; /* form error * 3 */
1057 errorptr[1] = (FSERROR) (bpreverr1 + cur1);
1058 cur1 += delta; /* form error * 5 */
1059 bpreverr1 = belowerr1 + cur1;
1060 belowerr1 = bnexterr;
1061 cur1 += delta; /* form error * 7 */
1062 bnexterr = cur2; /* Process component 2 */
1063 delta = cur2 * 2;
1064 cur2 += delta; /* form error * 3 */
1065 errorptr[2] = (FSERROR) (bpreverr2 + cur2);
1066 cur2 += delta; /* form error * 5 */
1067 bpreverr2 = belowerr2 + cur2;
1068 belowerr2 = bnexterr;
1069 cur2 += delta; /* form error * 7 */
1070 }
1071 /* At this point curN contains the 7/16 error value to be propagated
1072 * to the next pixel on the current line, and all the errors for the
1073 * next line have been shifted over. We are therefore ready to move on.
1074 */
1075 inptr += dir3; /* Advance pixel pointers to next column */
1076 outptr += dir;
1077 errorptr += dir3; /* advance errorptr to current column */
1078 }
1079 /* Post-loop cleanup: we must unload the final error values into the
1080 * final fserrors[] entry. Note we need not unload belowerrN because
1081 * it is for the dummy column before or after the actual array.
1082 */
1083 errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
1084 errorptr[1] = (FSERROR) bpreverr1;
1085 errorptr[2] = (FSERROR) bpreverr2;
1086 }
1087 }
1088
1089
1090 /*
1091 * Initialize the error-limiting transfer function (lookup table).
1092 * The raw F-S error computation can potentially compute error values of up to
1093 * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
1094 * much less, otherwise obviously wrong pixels will be created. (Typical
1095 * effects include weird fringes at color-area boundaries, isolated bright
1096 * pixels in a dark area, etc.) The standard advice for avoiding this problem
1097 * is to ensure that the "corners" of the color cube are allocated as output
1098 * colors; then repeated errors in the same direction cannot cause cascading
1099 * error buildup. However, that only prevents the error from getting
1100 * completely out of hand; Aaron Giles reports that error limiting improves
1101 * the results even with corner colors allocated.
1102 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1103 * well, but the smoother transfer function used below is even better. Thanks
1104 * to Aaron Giles for this idea.
1105 */
1106
1107 LOCAL(void)
1108 init_error_limit (j_decompress_ptr cinfo)
1109 /* Allocate and fill in the error_limiter table */
1110 {
1111 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1112 int * table;
1113 int in, out;
1114
1115 table = (int *) (*cinfo->mem->alloc_small)
1116 ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
1117 table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1118 cquantize->error_limiter = table;
1119
1120 #define STEPSIZE ((MAXJSAMPLE+1)/16)
1121 /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1122 out = 0;
1123 for (in = 0; in < STEPSIZE; in++, out++) {
1124 table[in] = out; table[-in] = -out;
1125 }
1126 /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1127 for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
1128 table[in] = out; table[-in] = -out;
1129 }
1130 /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1131 for (; in <= MAXJSAMPLE; in++) {
1132 table[in] = out; table[-in] = -out;
1133 }
1134 #undef STEPSIZE
1135 }
1136
1137
1138 /*
1139 * Finish up at the end of each pass.
1140 */
1141
1142 METHODDEF(void)
1143 finish_pass1 (j_decompress_ptr cinfo)
1144 {
1145 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1146
1147 /* Select the representative colors and fill in cinfo->colormap */
1148 cinfo->colormap = cquantize->sv_colormap;
1149 select_colors(cinfo, cquantize->desired);
1150 /* Force next pass to zero the color index table */
1151 cquantize->needs_zeroed = TRUE;
1152 }
1153
1154
1155 METHODDEF(void)
1156 finish_pass2 (j_decompress_ptr cinfo)
1157 {
1158 /* no work */
1159 }
1160
1161
1162 /*
1163 * Initialize for each processing pass.
1164 */
1165
1166 METHODDEF(void)
1167 start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
1168 {
1169 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1170 hist3d histogram = cquantize->histogram;
1171 int i;
1172
1173 /* Only F-S dithering or no dithering is supported. */
1174 /* If user asks for ordered dither, give him F-S. */
1175 if (cinfo->dither_mode != JDITHER_NONE)
1176 cinfo->dither_mode = JDITHER_FS;
1177
1178 if (is_pre_scan) {
1179 /* Set up method pointers */
1180 cquantize->pub.color_quantize = prescan_quantize;
1181 cquantize->pub.finish_pass = finish_pass1;
1182 cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1183 } else {
1184 /* Set up method pointers */
1185 if (cinfo->dither_mode == JDITHER_FS)
1186 cquantize->pub.color_quantize = pass2_fs_dither;
1187 else
1188 cquantize->pub.color_quantize = pass2_no_dither;
1189 cquantize->pub.finish_pass = finish_pass2;
1190
1191 /* Make sure color count is acceptable */
1192 i = cinfo->actual_number_of_colors;
1193 if (i < 1)
1194 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1195 if (i > MAXNUMCOLORS)
1196 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1197
1198 if (cinfo->dither_mode == JDITHER_FS) {
1199 size_t arraysize = (size_t) ((cinfo->output_width + 2) *
1200 (3 * SIZEOF(FSERROR)));
1201 /* Allocate Floyd-Steinberg workspace if we didn't already. */
1202 if (cquantize->fserrors == NULL)
1203 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1204 ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
1205 /* Initialize the propagated errors to zero. */
1206 jzero_far((void FAR *) cquantize->fserrors, arraysize);
1207 /* Make the error-limit table if we didn't already. */
1208 if (cquantize->error_limiter == NULL)
1209 init_error_limit(cinfo);
1210 cquantize->on_odd_row = FALSE;
1211 }
1212
1213 }
1214 /* Zero the histogram or inverse color map, if necessary */
1215 if (cquantize->needs_zeroed) {
1216 for (i = 0; i < HIST_C0_ELEMS; i++) {
1217 jzero_far((void FAR *) histogram[i],
1218 HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1219 }
1220 cquantize->needs_zeroed = FALSE;
1221 }
1222 }
1223
1224
1225 /*
1226 * Switch to a new external colormap between output passes.
1227 */
1228
1229 METHODDEF(void)
1230 new_color_map_2_quant (j_decompress_ptr cinfo)
1231 {
1232 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1233
1234 /* Reset the inverse color map */
1235 cquantize->needs_zeroed = TRUE;
1236 }
1237
1238
1239 /*
1240 * Module initialization routine for 2-pass color quantization.
1241 */
1242
1243 GLOBAL(void)
1244 jinit_2pass_quantizer (j_decompress_ptr cinfo)
1245 {
1246 my_cquantize_ptr cquantize;
1247 int i;
1248
1249 cquantize = (my_cquantize_ptr)
1250 (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
1251 SIZEOF(my_cquantizer));
1252 cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
1253 cquantize->pub.start_pass = start_pass_2_quant;
1254 cquantize->pub.new_color_map = new_color_map_2_quant;
1255 cquantize->fserrors = NULL; /* flag optional arrays not allocated */
1256 cquantize->error_limiter = NULL;
1257
1258 /* Make sure jdmaster didn't give me a case I can't handle */
1259 if (cinfo->out_color_components != 3)
1260 ERREXIT(cinfo, JERR_NOTIMPL);
1261
1262 /* Allocate the histogram/inverse colormap storage */
1263 cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
1264 ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
1265 for (i = 0; i < HIST_C0_ELEMS; i++) {
1266 cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
1267 ((j_common_ptr) cinfo, JPOOL_IMAGE,
1268 HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1269 }
1270 cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1271
1272 /* Allocate storage for the completed colormap, if required.
1273 * We do this now since it is FAR storage and may affect
1274 * the memory manager's space calculations.
1275 */
1276 if (cinfo->enable_2pass_quant) {
1277 /* Make sure color count is acceptable */
1278 int desired = cinfo->desired_number_of_colors;
1279 /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1280 if (desired < 8)
1281 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1282 /* Make sure colormap indexes can be represented by JSAMPLEs */
1283 if (desired > MAXNUMCOLORS)
1284 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1285 cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1286 ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
1287 cquantize->desired = desired;
1288 } else
1289 cquantize->sv_colormap = NULL;
1290
1291 /* Only F-S dithering or no dithering is supported. */
1292 /* If user asks for ordered dither, give him F-S. */
1293 if (cinfo->dither_mode != JDITHER_NONE)
1294 cinfo->dither_mode = JDITHER_FS;
1295
1296 /* Allocate Floyd-Steinberg workspace if necessary.
1297 * This isn't really needed until pass 2, but again it is FAR storage.
1298 * Although we will cope with a later change in dither_mode,
1299 * we do not promise to honor max_memory_to_use if dither_mode changes.
1300 */
1301 if (cinfo->dither_mode == JDITHER_FS) {
1302 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1303 ((j_common_ptr) cinfo, JPOOL_IMAGE,
1304 (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
1305 /* Might as well create the error-limiting table too. */
1306 init_error_limit(cinfo);
1307 }
1308 }
1309
1310 #endif /* QUANT_2PASS_SUPPORTED */

  ViewVC Help
Powered by ViewVC 1.1.22