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author | David Walter Seikel | 2013-01-13 17:24:39 +1000 |
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committer | David Walter Seikel | 2013-01-13 17:24:39 +1000 |
commit | 393b5cd1dc438872af89d334ef6e5fcc59f27d47 (patch) | |
tree | 6a14521219942a08a1b95cb2f5a923a9edd60f63 /libraries/irrlicht-1.8/source/Irrlicht/jpeglib/jquant2.c | |
parent | Add a note about rasters suggested start up code. (diff) | |
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Added Irrlicht 1.8, but without all the Windows binaries.
Diffstat (limited to 'libraries/irrlicht-1.8/source/Irrlicht/jpeglib/jquant2.c')
-rw-r--r-- | libraries/irrlicht-1.8/source/Irrlicht/jpeglib/jquant2.c | 1311 |
1 files changed, 1311 insertions, 0 deletions
diff --git a/libraries/irrlicht-1.8/source/Irrlicht/jpeglib/jquant2.c b/libraries/irrlicht-1.8/source/Irrlicht/jpeglib/jquant2.c new file mode 100644 index 0000000..f7e351f --- /dev/null +++ b/libraries/irrlicht-1.8/source/Irrlicht/jpeglib/jquant2.c | |||
@@ -0,0 +1,1311 @@ | |||
1 | /* | ||
2 | * jquant2.c | ||
3 | * | ||
4 | * Copyright (C) 1991-1996, Thomas G. Lane. | ||
5 | * Modified 2011 by Guido Vollbeding. | ||
6 | * This file is part of the Independent JPEG Group's software. | ||
7 | * For conditions of distribution and use, see the accompanying README file. | ||
8 | * | ||
9 | * This file contains 2-pass color quantization (color mapping) routines. | ||
10 | * These routines provide selection of a custom color map for an image, | ||
11 | * followed by mapping of the image to that color map, with optional | ||
12 | * Floyd-Steinberg dithering. | ||
13 | * It is also possible to use just the second pass to map to an arbitrary | ||
14 | * externally-given color map. | ||
15 | * | ||
16 | * Note: ordered dithering is not supported, since there isn't any fast | ||
17 | * way to compute intercolor distances; it's unclear that ordered dither's | ||
18 | * fundamental assumptions even hold with an irregularly spaced color map. | ||
19 | */ | ||
20 | |||
21 | #define JPEG_INTERNALS | ||
22 | #include "jinclude.h" | ||
23 | #include "jpeglib.h" | ||
24 | |||
25 | #ifdef QUANT_2PASS_SUPPORTED | ||
26 | |||
27 | |||
28 | /* | ||
29 | * This module implements the well-known Heckbert paradigm for color | ||
30 | * quantization. Most of the ideas used here can be traced back to | ||
31 | * Heckbert's seminal paper | ||
32 | * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display", | ||
33 | * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304. | ||
34 | * | ||
35 | * In the first pass over the image, we accumulate a histogram showing the | ||
36 | * usage count of each possible color. To keep the histogram to a reasonable | ||
37 | * size, we reduce the precision of the input; typical practice is to retain | ||
38 | * 5 or 6 bits per color, so that 8 or 4 different input values are counted | ||
39 | * in the same histogram cell. | ||
40 | * | ||
41 | * Next, the color-selection step begins with a box representing the whole | ||
42 | * color space, and repeatedly splits the "largest" remaining box until we | ||
43 | * have as many boxes as desired colors. Then the mean color in each | ||
44 | * remaining box becomes one of the possible output colors. | ||
45 | * | ||
46 | * The second pass over the image maps each input pixel to the closest output | ||
47 | * color (optionally after applying a Floyd-Steinberg dithering correction). | ||
48 | * This mapping is logically trivial, but making it go fast enough requires | ||
49 | * considerable care. | ||
50 | * | ||
51 | * Heckbert-style quantizers vary a good deal in their policies for choosing | ||
52 | * the "largest" box and deciding where to cut it. The particular policies | ||
53 | * used here have proved out well in experimental comparisons, but better ones | ||
54 | * may yet be found. | ||
55 | * | ||
56 | * In earlier versions of the IJG code, this module quantized in YCbCr color | ||
57 | * space, processing the raw upsampled data without a color conversion step. | ||
58 | * This allowed the color conversion math to be done only once per colormap | ||
59 | * entry, not once per pixel. However, that optimization precluded other | ||
60 | * useful optimizations (such as merging color conversion with upsampling) | ||
61 | * and it also interfered with desired capabilities such as quantizing to an | ||
62 | * externally-supplied colormap. We have therefore abandoned that approach. | ||
63 | * The present code works in the post-conversion color space, typically RGB. | ||
64 | * | ||
65 | * To improve the visual quality of the results, we actually work in scaled | ||
66 | * RGB space, giving G distances more weight than R, and R in turn more than | ||
67 | * B. To do everything in integer math, we must use integer scale factors. | ||
68 | * The 2/3/1 scale factors used here correspond loosely to the relative | ||
69 | * weights of the colors in the NTSC grayscale equation. | ||
70 | * If you want to use this code to quantize a non-RGB color space, you'll | ||
71 | * probably need to change these scale factors. | ||
72 | */ | ||
73 | |||
74 | #define R_SCALE 2 /* scale R distances by this much */ | ||
75 | #define G_SCALE 3 /* scale G distances by this much */ | ||
76 | #define B_SCALE 1 /* and B by this much */ | ||
77 | |||
78 | /* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined | ||
79 | * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B | ||
80 | * and B,G,R orders. If you define some other weird order in jmorecfg.h, | ||
81 | * you'll get compile errors until you extend this logic. In that case | ||
82 | * you'll probably want to tweak the histogram sizes too. | ||
83 | */ | ||
84 | |||
85 | #if RGB_RED == 0 | ||
86 | #define C0_SCALE R_SCALE | ||
87 | #endif | ||
88 | #if RGB_BLUE == 0 | ||
89 | #define C0_SCALE B_SCALE | ||
90 | #endif | ||
91 | #if RGB_GREEN == 1 | ||
92 | #define C1_SCALE G_SCALE | ||
93 | #endif | ||
94 | #if RGB_RED == 2 | ||
95 | #define C2_SCALE R_SCALE | ||
96 | #endif | ||
97 | #if RGB_BLUE == 2 | ||
98 | #define C2_SCALE B_SCALE | ||
99 | #endif | ||
100 | |||
101 | |||
102 | /* | ||
103 | * First we have the histogram data structure and routines for creating it. | ||
104 | * | ||
105 | * The number of bits of precision can be adjusted by changing these symbols. | ||
106 | * We recommend keeping 6 bits for G and 5 each for R and B. | ||
107 | * If you have plenty of memory and cycles, 6 bits all around gives marginally | ||
108 | * better results; if you are short of memory, 5 bits all around will save | ||
109 | * some space but degrade the results. | ||
110 | * To maintain a fully accurate histogram, we'd need to allocate a "long" | ||
111 | * (preferably unsigned long) for each cell. In practice this is overkill; | ||
112 | * we can get by with 16 bits per cell. Few of the cell counts will overflow, | ||
113 | * and clamping those that do overflow to the maximum value will give close- | ||
114 | * enough results. This reduces the recommended histogram size from 256Kb | ||
115 | * to 128Kb, which is a useful savings on PC-class machines. | ||
116 | * (In the second pass the histogram space is re-used for pixel mapping data; | ||
117 | * in that capacity, each cell must be able to store zero to the number of | ||
118 | * desired colors. 16 bits/cell is plenty for that too.) | ||
119 | * Since the JPEG code is intended to run in small memory model on 80x86 | ||
120 | * machines, we can't just allocate the histogram in one chunk. Instead | ||
121 | * of a true 3-D array, we use a row of pointers to 2-D arrays. Each | ||
122 | * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and | ||
123 | * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that | ||
124 | * on 80x86 machines, the pointer row is in near memory but the actual | ||
125 | * arrays are in far memory (same arrangement as we use for image arrays). | ||
126 | */ | ||
127 | |||
128 | #define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */ | ||
129 | |||
130 | /* These will do the right thing for either R,G,B or B,G,R color order, | ||
131 | * but you may not like the results for other color orders. | ||
132 | */ | ||
133 | #define HIST_C0_BITS 5 /* bits of precision in R/B histogram */ | ||
134 | #define HIST_C1_BITS 6 /* bits of precision in G histogram */ | ||
135 | #define HIST_C2_BITS 5 /* bits of precision in B/R histogram */ | ||
136 | |||
137 | /* Number of elements along histogram axes. */ | ||
138 | #define HIST_C0_ELEMS (1<<HIST_C0_BITS) | ||
139 | #define HIST_C1_ELEMS (1<<HIST_C1_BITS) | ||
140 | #define HIST_C2_ELEMS (1<<HIST_C2_BITS) | ||
141 | |||
142 | /* These are the amounts to shift an input value to get a histogram index. */ | ||
143 | #define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS) | ||
144 | #define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS) | ||
145 | #define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS) | ||
146 | |||
147 | |||
148 | typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */ | ||
149 | |||
150 | typedef histcell FAR * histptr; /* for pointers to histogram cells */ | ||
151 | |||
152 | typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */ | ||
153 | typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */ | ||
154 | typedef hist2d * hist3d; /* type for top-level pointer */ | ||
155 | |||
156 | |||
157 | /* Declarations for Floyd-Steinberg dithering. | ||
158 | * | ||
159 | * Errors are accumulated into the array fserrors[], at a resolution of | ||
160 | * 1/16th of a pixel count. The error at a given pixel is propagated | ||
161 | * to its not-yet-processed neighbors using the standard F-S fractions, | ||
162 | * ... (here) 7/16 | ||
163 | * 3/16 5/16 1/16 | ||
164 | * We work left-to-right on even rows, right-to-left on odd rows. | ||
165 | * | ||
166 | * We can get away with a single array (holding one row's worth of errors) | ||
167 | * by using it to store the current row's errors at pixel columns not yet | ||
168 | * processed, but the next row's errors at columns already processed. We | ||
169 | * need only a few extra variables to hold the errors immediately around the | ||
170 | * current column. (If we are lucky, those variables are in registers, but | ||
171 | * even if not, they're probably cheaper to access than array elements are.) | ||
172 | * | ||
173 | * The fserrors[] array has (#columns + 2) entries; the extra entry at | ||
174 | * each end saves us from special-casing the first and last pixels. | ||
175 | * Each entry is three values long, one value for each color component. | ||
176 | * | ||
177 | * Note: on a wide image, we might not have enough room in a PC's near data | ||
178 | * segment to hold the error array; so it is allocated with alloc_large. | ||
179 | */ | ||
180 | |||
181 | #if BITS_IN_JSAMPLE == 8 | ||
182 | typedef INT16 FSERROR; /* 16 bits should be enough */ | ||
183 | typedef int LOCFSERROR; /* use 'int' for calculation temps */ | ||
184 | #else | ||
185 | typedef INT32 FSERROR; /* may need more than 16 bits */ | ||
186 | typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */ | ||
187 | #endif | ||
188 | |||
189 | typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */ | ||
190 | |||
191 | |||
192 | /* Private subobject */ | ||
193 | |||
194 | typedef struct { | ||
195 | struct jpeg_color_quantizer pub; /* public fields */ | ||
196 | |||
197 | /* Space for the eventually created colormap is stashed here */ | ||
198 | JSAMPARRAY sv_colormap; /* colormap allocated at init time */ | ||
199 | int desired; /* desired # of colors = size of colormap */ | ||
200 | |||
201 | /* Variables for accumulating image statistics */ | ||
202 | hist3d histogram; /* pointer to the histogram */ | ||
203 | |||
204 | boolean needs_zeroed; /* TRUE if next pass must zero histogram */ | ||
205 | |||
206 | /* Variables for Floyd-Steinberg dithering */ | ||
207 | FSERRPTR fserrors; /* accumulated errors */ | ||
208 | boolean on_odd_row; /* flag to remember which row we are on */ | ||
209 | int * error_limiter; /* table for clamping the applied error */ | ||
210 | } my_cquantizer; | ||
211 | |||
212 | typedef my_cquantizer * my_cquantize_ptr; | ||
213 | |||
214 | |||
215 | /* | ||
216 | * Prescan some rows of pixels. | ||
217 | * In this module the prescan simply updates the histogram, which has been | ||
218 | * initialized to zeroes by start_pass. | ||
219 | * An output_buf parameter is required by the method signature, but no data | ||
220 | * is actually output (in fact the buffer controller is probably passing a | ||
221 | * NULL pointer). | ||
222 | */ | ||
223 | |||
224 | METHODDEF(void) | ||
225 | prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf, | ||
226 | JSAMPARRAY output_buf, int num_rows) | ||
227 | { | ||
228 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
229 | register JSAMPROW ptr; | ||
230 | register histptr histp; | ||
231 | register hist3d histogram = cquantize->histogram; | ||
232 | int row; | ||
233 | JDIMENSION col; | ||
234 | JDIMENSION width = cinfo->output_width; | ||
235 | |||
236 | for (row = 0; row < num_rows; row++) { | ||
237 | ptr = input_buf[row]; | ||
238 | for (col = width; col > 0; col--) { | ||
239 | /* get pixel value and index into the histogram */ | ||
240 | histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT] | ||
241 | [GETJSAMPLE(ptr[1]) >> C1_SHIFT] | ||
242 | [GETJSAMPLE(ptr[2]) >> C2_SHIFT]; | ||
243 | /* increment, check for overflow and undo increment if so. */ | ||
244 | if (++(*histp) <= 0) | ||
245 | (*histp)--; | ||
246 | ptr += 3; | ||
247 | } | ||
248 | } | ||
249 | } | ||
250 | |||
251 | |||
252 | /* | ||
253 | * Next we have the really interesting routines: selection of a colormap | ||
254 | * given the completed histogram. | ||
255 | * These routines work with a list of "boxes", each representing a rectangular | ||
256 | * subset of the input color space (to histogram precision). | ||
257 | */ | ||
258 | |||
259 | typedef struct { | ||
260 | /* The bounds of the box (inclusive); expressed as histogram indexes */ | ||
261 | int c0min, c0max; | ||
262 | int c1min, c1max; | ||
263 | int c2min, c2max; | ||
264 | /* The volume (actually 2-norm) of the box */ | ||
265 | INT32 volume; | ||
266 | /* The number of nonzero histogram cells within this box */ | ||
267 | long colorcount; | ||
268 | } box; | ||
269 | |||
270 | typedef box * boxptr; | ||
271 | |||
272 | |||
273 | LOCAL(boxptr) | ||
274 | find_biggest_color_pop (boxptr boxlist, int numboxes) | ||
275 | /* Find the splittable box with the largest color population */ | ||
276 | /* Returns NULL if no splittable boxes remain */ | ||
277 | { | ||
278 | register boxptr boxp; | ||
279 | register int i; | ||
280 | register long maxc = 0; | ||
281 | boxptr which = NULL; | ||
282 | |||
283 | for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { | ||
284 | if (boxp->colorcount > maxc && boxp->volume > 0) { | ||
285 | which = boxp; | ||
286 | maxc = boxp->colorcount; | ||
287 | } | ||
288 | } | ||
289 | return which; | ||
290 | } | ||
291 | |||
292 | |||
293 | LOCAL(boxptr) | ||
294 | find_biggest_volume (boxptr boxlist, int numboxes) | ||
295 | /* Find the splittable box with the largest (scaled) volume */ | ||
296 | /* Returns NULL if no splittable boxes remain */ | ||
297 | { | ||
298 | register boxptr boxp; | ||
299 | register int i; | ||
300 | register INT32 maxv = 0; | ||
301 | boxptr which = NULL; | ||
302 | |||
303 | for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { | ||
304 | if (boxp->volume > maxv) { | ||
305 | which = boxp; | ||
306 | maxv = boxp->volume; | ||
307 | } | ||
308 | } | ||
309 | return which; | ||
310 | } | ||
311 | |||
312 | |||
313 | LOCAL(void) | ||
314 | update_box (j_decompress_ptr cinfo, boxptr boxp) | ||
315 | /* Shrink the min/max bounds of a box to enclose only nonzero elements, */ | ||
316 | /* and recompute its volume and population */ | ||
317 | { | ||
318 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
319 | hist3d histogram = cquantize->histogram; | ||
320 | histptr histp; | ||
321 | int c0,c1,c2; | ||
322 | int c0min,c0max,c1min,c1max,c2min,c2max; | ||
323 | INT32 dist0,dist1,dist2; | ||
324 | long ccount; | ||
325 | |||
326 | c0min = boxp->c0min; c0max = boxp->c0max; | ||
327 | c1min = boxp->c1min; c1max = boxp->c1max; | ||
328 | c2min = boxp->c2min; c2max = boxp->c2max; | ||
329 | |||
330 | if (c0max > c0min) | ||
331 | for (c0 = c0min; c0 <= c0max; c0++) | ||
332 | for (c1 = c1min; c1 <= c1max; c1++) { | ||
333 | histp = & histogram[c0][c1][c2min]; | ||
334 | for (c2 = c2min; c2 <= c2max; c2++) | ||
335 | if (*histp++ != 0) { | ||
336 | boxp->c0min = c0min = c0; | ||
337 | goto have_c0min; | ||
338 | } | ||
339 | } | ||
340 | have_c0min: | ||
341 | if (c0max > c0min) | ||
342 | for (c0 = c0max; c0 >= c0min; c0--) | ||
343 | for (c1 = c1min; c1 <= c1max; c1++) { | ||
344 | histp = & histogram[c0][c1][c2min]; | ||
345 | for (c2 = c2min; c2 <= c2max; c2++) | ||
346 | if (*histp++ != 0) { | ||
347 | boxp->c0max = c0max = c0; | ||
348 | goto have_c0max; | ||
349 | } | ||
350 | } | ||
351 | have_c0max: | ||
352 | if (c1max > c1min) | ||
353 | for (c1 = c1min; c1 <= c1max; c1++) | ||
354 | for (c0 = c0min; c0 <= c0max; c0++) { | ||
355 | histp = & histogram[c0][c1][c2min]; | ||
356 | for (c2 = c2min; c2 <= c2max; c2++) | ||
357 | if (*histp++ != 0) { | ||
358 | boxp->c1min = c1min = c1; | ||
359 | goto have_c1min; | ||
360 | } | ||
361 | } | ||
362 | have_c1min: | ||
363 | if (c1max > c1min) | ||
364 | for (c1 = c1max; c1 >= c1min; c1--) | ||
365 | for (c0 = c0min; c0 <= c0max; c0++) { | ||
366 | histp = & histogram[c0][c1][c2min]; | ||
367 | for (c2 = c2min; c2 <= c2max; c2++) | ||
368 | if (*histp++ != 0) { | ||
369 | boxp->c1max = c1max = c1; | ||
370 | goto have_c1max; | ||
371 | } | ||
372 | } | ||
373 | have_c1max: | ||
374 | if (c2max > c2min) | ||
375 | for (c2 = c2min; c2 <= c2max; c2++) | ||
376 | for (c0 = c0min; c0 <= c0max; c0++) { | ||
377 | histp = & histogram[c0][c1min][c2]; | ||
378 | for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) | ||
379 | if (*histp != 0) { | ||
380 | boxp->c2min = c2min = c2; | ||
381 | goto have_c2min; | ||
382 | } | ||
383 | } | ||
384 | have_c2min: | ||
385 | if (c2max > c2min) | ||
386 | for (c2 = c2max; c2 >= c2min; c2--) | ||
387 | for (c0 = c0min; c0 <= c0max; c0++) { | ||
388 | histp = & histogram[c0][c1min][c2]; | ||
389 | for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) | ||
390 | if (*histp != 0) { | ||
391 | boxp->c2max = c2max = c2; | ||
392 | goto have_c2max; | ||
393 | } | ||
394 | } | ||
395 | have_c2max: | ||
396 | |||
397 | /* Update box volume. | ||
398 | * We use 2-norm rather than real volume here; this biases the method | ||
399 | * against making long narrow boxes, and it has the side benefit that | ||
400 | * a box is splittable iff norm > 0. | ||
401 | * Since the differences are expressed in histogram-cell units, | ||
402 | * we have to shift back to JSAMPLE units to get consistent distances; | ||
403 | * after which, we scale according to the selected distance scale factors. | ||
404 | */ | ||
405 | dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; | ||
406 | dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; | ||
407 | dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; | ||
408 | boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2; | ||
409 | |||
410 | /* Now scan remaining volume of box and compute population */ | ||
411 | ccount = 0; | ||
412 | for (c0 = c0min; c0 <= c0max; c0++) | ||
413 | for (c1 = c1min; c1 <= c1max; c1++) { | ||
414 | histp = & histogram[c0][c1][c2min]; | ||
415 | for (c2 = c2min; c2 <= c2max; c2++, histp++) | ||
416 | if (*histp != 0) { | ||
417 | ccount++; | ||
418 | } | ||
419 | } | ||
420 | boxp->colorcount = ccount; | ||
421 | } | ||
422 | |||
423 | |||
424 | LOCAL(int) | ||
425 | median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes, | ||
426 | int desired_colors) | ||
427 | /* Repeatedly select and split the largest box until we have enough boxes */ | ||
428 | { | ||
429 | int n,lb; | ||
430 | int c0,c1,c2,cmax; | ||
431 | register boxptr b1,b2; | ||
432 | |||
433 | while (numboxes < desired_colors) { | ||
434 | /* Select box to split. | ||
435 | * Current algorithm: by population for first half, then by volume. | ||
436 | */ | ||
437 | if (numboxes*2 <= desired_colors) { | ||
438 | b1 = find_biggest_color_pop(boxlist, numboxes); | ||
439 | } else { | ||
440 | b1 = find_biggest_volume(boxlist, numboxes); | ||
441 | } | ||
442 | if (b1 == NULL) /* no splittable boxes left! */ | ||
443 | break; | ||
444 | b2 = &boxlist[numboxes]; /* where new box will go */ | ||
445 | /* Copy the color bounds to the new box. */ | ||
446 | b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max; | ||
447 | b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min; | ||
448 | /* Choose which axis to split the box on. | ||
449 | * Current algorithm: longest scaled axis. | ||
450 | * See notes in update_box about scaling distances. | ||
451 | */ | ||
452 | c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE; | ||
453 | c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE; | ||
454 | c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE; | ||
455 | /* We want to break any ties in favor of green, then red, blue last. | ||
456 | * This code does the right thing for R,G,B or B,G,R color orders only. | ||
457 | */ | ||
458 | #if RGB_RED == 0 | ||
459 | cmax = c1; n = 1; | ||
460 | if (c0 > cmax) { cmax = c0; n = 0; } | ||
461 | if (c2 > cmax) { n = 2; } | ||
462 | #else | ||
463 | cmax = c1; n = 1; | ||
464 | if (c2 > cmax) { cmax = c2; n = 2; } | ||
465 | if (c0 > cmax) { n = 0; } | ||
466 | #endif | ||
467 | /* Choose split point along selected axis, and update box bounds. | ||
468 | * Current algorithm: split at halfway point. | ||
469 | * (Since the box has been shrunk to minimum volume, | ||
470 | * any split will produce two nonempty subboxes.) | ||
471 | * Note that lb value is max for lower box, so must be < old max. | ||
472 | */ | ||
473 | switch (n) { | ||
474 | case 0: | ||
475 | lb = (b1->c0max + b1->c0min) / 2; | ||
476 | b1->c0max = lb; | ||
477 | b2->c0min = lb+1; | ||
478 | break; | ||
479 | case 1: | ||
480 | lb = (b1->c1max + b1->c1min) / 2; | ||
481 | b1->c1max = lb; | ||
482 | b2->c1min = lb+1; | ||
483 | break; | ||
484 | case 2: | ||
485 | lb = (b1->c2max + b1->c2min) / 2; | ||
486 | b1->c2max = lb; | ||
487 | b2->c2min = lb+1; | ||
488 | break; | ||
489 | } | ||
490 | /* Update stats for boxes */ | ||
491 | update_box(cinfo, b1); | ||
492 | update_box(cinfo, b2); | ||
493 | numboxes++; | ||
494 | } | ||
495 | return numboxes; | ||
496 | } | ||
497 | |||
498 | |||
499 | LOCAL(void) | ||
500 | compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor) | ||
501 | /* Compute representative color for a box, put it in colormap[icolor] */ | ||
502 | { | ||
503 | /* Current algorithm: mean weighted by pixels (not colors) */ | ||
504 | /* Note it is important to get the rounding correct! */ | ||
505 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
506 | hist3d histogram = cquantize->histogram; | ||
507 | histptr histp; | ||
508 | int c0,c1,c2; | ||
509 | int c0min,c0max,c1min,c1max,c2min,c2max; | ||
510 | long count; | ||
511 | long total = 0; | ||
512 | long c0total = 0; | ||
513 | long c1total = 0; | ||
514 | long c2total = 0; | ||
515 | |||
516 | c0min = boxp->c0min; c0max = boxp->c0max; | ||
517 | c1min = boxp->c1min; c1max = boxp->c1max; | ||
518 | c2min = boxp->c2min; c2max = boxp->c2max; | ||
519 | |||
520 | for (c0 = c0min; c0 <= c0max; c0++) | ||
521 | for (c1 = c1min; c1 <= c1max; c1++) { | ||
522 | histp = & histogram[c0][c1][c2min]; | ||
523 | for (c2 = c2min; c2 <= c2max; c2++) { | ||
524 | if ((count = *histp++) != 0) { | ||
525 | total += count; | ||
526 | c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count; | ||
527 | c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count; | ||
528 | c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count; | ||
529 | } | ||
530 | } | ||
531 | } | ||
532 | |||
533 | cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); | ||
534 | cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); | ||
535 | cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); | ||
536 | } | ||
537 | |||
538 | |||
539 | LOCAL(void) | ||
540 | select_colors (j_decompress_ptr cinfo, int desired_colors) | ||
541 | /* Master routine for color selection */ | ||
542 | { | ||
543 | boxptr boxlist; | ||
544 | int numboxes; | ||
545 | int i; | ||
546 | |||
547 | /* Allocate workspace for box list */ | ||
548 | boxlist = (boxptr) (*cinfo->mem->alloc_small) | ||
549 | ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box)); | ||
550 | /* Initialize one box containing whole space */ | ||
551 | numboxes = 1; | ||
552 | boxlist[0].c0min = 0; | ||
553 | boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; | ||
554 | boxlist[0].c1min = 0; | ||
555 | boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; | ||
556 | boxlist[0].c2min = 0; | ||
557 | boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; | ||
558 | /* Shrink it to actually-used volume and set its statistics */ | ||
559 | update_box(cinfo, & boxlist[0]); | ||
560 | /* Perform median-cut to produce final box list */ | ||
561 | numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors); | ||
562 | /* Compute the representative color for each box, fill colormap */ | ||
563 | for (i = 0; i < numboxes; i++) | ||
564 | compute_color(cinfo, & boxlist[i], i); | ||
565 | cinfo->actual_number_of_colors = numboxes; | ||
566 | TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes); | ||
567 | } | ||
568 | |||
569 | |||
570 | /* | ||
571 | * These routines are concerned with the time-critical task of mapping input | ||
572 | * colors to the nearest color in the selected colormap. | ||
573 | * | ||
574 | * We re-use the histogram space as an "inverse color map", essentially a | ||
575 | * cache for the results of nearest-color searches. All colors within a | ||
576 | * histogram cell will be mapped to the same colormap entry, namely the one | ||
577 | * closest to the cell's center. This may not be quite the closest entry to | ||
578 | * the actual input color, but it's almost as good. A zero in the cache | ||
579 | * indicates we haven't found the nearest color for that cell yet; the array | ||
580 | * is cleared to zeroes before starting the mapping pass. When we find the | ||
581 | * nearest color for a cell, its colormap index plus one is recorded in the | ||
582 | * cache for future use. The pass2 scanning routines call fill_inverse_cmap | ||
583 | * when they need to use an unfilled entry in the cache. | ||
584 | * | ||
585 | * Our method of efficiently finding nearest colors is based on the "locally | ||
586 | * sorted search" idea described by Heckbert and on the incremental distance | ||
587 | * calculation described by Spencer W. Thomas in chapter III.1 of Graphics | ||
588 | * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that | ||
589 | * the distances from a given colormap entry to each cell of the histogram can | ||
590 | * be computed quickly using an incremental method: the differences between | ||
591 | * distances to adjacent cells themselves differ by a constant. This allows a | ||
592 | * fairly fast implementation of the "brute force" approach of computing the | ||
593 | * distance from every colormap entry to every histogram cell. Unfortunately, | ||
594 | * it needs a work array to hold the best-distance-so-far for each histogram | ||
595 | * cell (because the inner loop has to be over cells, not colormap entries). | ||
596 | * The work array elements have to be INT32s, so the work array would need | ||
597 | * 256Kb at our recommended precision. This is not feasible in DOS machines. | ||
598 | * | ||
599 | * To get around these problems, we apply Thomas' method to compute the | ||
600 | * nearest colors for only the cells within a small subbox of the histogram. | ||
601 | * The work array need be only as big as the subbox, so the memory usage | ||
602 | * problem is solved. Furthermore, we need not fill subboxes that are never | ||
603 | * referenced in pass2; many images use only part of the color gamut, so a | ||
604 | * fair amount of work is saved. An additional advantage of this | ||
605 | * approach is that we can apply Heckbert's locality criterion to quickly | ||
606 | * eliminate colormap entries that are far away from the subbox; typically | ||
607 | * three-fourths of the colormap entries are rejected by Heckbert's criterion, | ||
608 | * and we need not compute their distances to individual cells in the subbox. | ||
609 | * The speed of this approach is heavily influenced by the subbox size: too | ||
610 | * small means too much overhead, too big loses because Heckbert's criterion | ||
611 | * can't eliminate as many colormap entries. Empirically the best subbox | ||
612 | * size seems to be about 1/512th of the histogram (1/8th in each direction). | ||
613 | * | ||
614 | * Thomas' article also describes a refined method which is asymptotically | ||
615 | * faster than the brute-force method, but it is also far more complex and | ||
616 | * cannot efficiently be applied to small subboxes. It is therefore not | ||
617 | * useful for programs intended to be portable to DOS machines. On machines | ||
618 | * with plenty of memory, filling the whole histogram in one shot with Thomas' | ||
619 | * refined method might be faster than the present code --- but then again, | ||
620 | * it might not be any faster, and it's certainly more complicated. | ||
621 | */ | ||
622 | |||
623 | |||
624 | /* log2(histogram cells in update box) for each axis; this can be adjusted */ | ||
625 | #define BOX_C0_LOG (HIST_C0_BITS-3) | ||
626 | #define BOX_C1_LOG (HIST_C1_BITS-3) | ||
627 | #define BOX_C2_LOG (HIST_C2_BITS-3) | ||
628 | |||
629 | #define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */ | ||
630 | #define BOX_C1_ELEMS (1<<BOX_C1_LOG) | ||
631 | #define BOX_C2_ELEMS (1<<BOX_C2_LOG) | ||
632 | |||
633 | #define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG) | ||
634 | #define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG) | ||
635 | #define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG) | ||
636 | |||
637 | |||
638 | /* | ||
639 | * The next three routines implement inverse colormap filling. They could | ||
640 | * all be folded into one big routine, but splitting them up this way saves | ||
641 | * some stack space (the mindist[] and bestdist[] arrays need not coexist) | ||
642 | * and may allow some compilers to produce better code by registerizing more | ||
643 | * inner-loop variables. | ||
644 | */ | ||
645 | |||
646 | LOCAL(int) | ||
647 | find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, | ||
648 | JSAMPLE colorlist[]) | ||
649 | /* Locate the colormap entries close enough to an update box to be candidates | ||
650 | * for the nearest entry to some cell(s) in the update box. The update box | ||
651 | * is specified by the center coordinates of its first cell. The number of | ||
652 | * candidate colormap entries is returned, and their colormap indexes are | ||
653 | * placed in colorlist[]. | ||
654 | * This routine uses Heckbert's "locally sorted search" criterion to select | ||
655 | * the colors that need further consideration. | ||
656 | */ | ||
657 | { | ||
658 | int numcolors = cinfo->actual_number_of_colors; | ||
659 | int maxc0, maxc1, maxc2; | ||
660 | int centerc0, centerc1, centerc2; | ||
661 | int i, x, ncolors; | ||
662 | INT32 minmaxdist, min_dist, max_dist, tdist; | ||
663 | INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */ | ||
664 | |||
665 | /* Compute true coordinates of update box's upper corner and center. | ||
666 | * Actually we compute the coordinates of the center of the upper-corner | ||
667 | * histogram cell, which are the upper bounds of the volume we care about. | ||
668 | * Note that since ">>" rounds down, the "center" values may be closer to | ||
669 | * min than to max; hence comparisons to them must be "<=", not "<". | ||
670 | */ | ||
671 | maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT)); | ||
672 | centerc0 = (minc0 + maxc0) >> 1; | ||
673 | maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT)); | ||
674 | centerc1 = (minc1 + maxc1) >> 1; | ||
675 | maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT)); | ||
676 | centerc2 = (minc2 + maxc2) >> 1; | ||
677 | |||
678 | /* For each color in colormap, find: | ||
679 | * 1. its minimum squared-distance to any point in the update box | ||
680 | * (zero if color is within update box); | ||
681 | * 2. its maximum squared-distance to any point in the update box. | ||
682 | * Both of these can be found by considering only the corners of the box. | ||
683 | * We save the minimum distance for each color in mindist[]; | ||
684 | * only the smallest maximum distance is of interest. | ||
685 | */ | ||
686 | minmaxdist = 0x7FFFFFFFL; | ||
687 | |||
688 | for (i = 0; i < numcolors; i++) { | ||
689 | /* We compute the squared-c0-distance term, then add in the other two. */ | ||
690 | x = GETJSAMPLE(cinfo->colormap[0][i]); | ||
691 | if (x < minc0) { | ||
692 | tdist = (x - minc0) * C0_SCALE; | ||
693 | min_dist = tdist*tdist; | ||
694 | tdist = (x - maxc0) * C0_SCALE; | ||
695 | max_dist = tdist*tdist; | ||
696 | } else if (x > maxc0) { | ||
697 | tdist = (x - maxc0) * C0_SCALE; | ||
698 | min_dist = tdist*tdist; | ||
699 | tdist = (x - minc0) * C0_SCALE; | ||
700 | max_dist = tdist*tdist; | ||
701 | } else { | ||
702 | /* within cell range so no contribution to min_dist */ | ||
703 | min_dist = 0; | ||
704 | if (x <= centerc0) { | ||
705 | tdist = (x - maxc0) * C0_SCALE; | ||
706 | max_dist = tdist*tdist; | ||
707 | } else { | ||
708 | tdist = (x - minc0) * C0_SCALE; | ||
709 | max_dist = tdist*tdist; | ||
710 | } | ||
711 | } | ||
712 | |||
713 | x = GETJSAMPLE(cinfo->colormap[1][i]); | ||
714 | if (x < minc1) { | ||
715 | tdist = (x - minc1) * C1_SCALE; | ||
716 | min_dist += tdist*tdist; | ||
717 | tdist = (x - maxc1) * C1_SCALE; | ||
718 | max_dist += tdist*tdist; | ||
719 | } else if (x > maxc1) { | ||
720 | tdist = (x - maxc1) * C1_SCALE; | ||
721 | min_dist += tdist*tdist; | ||
722 | tdist = (x - minc1) * C1_SCALE; | ||
723 | max_dist += tdist*tdist; | ||
724 | } else { | ||
725 | /* within cell range so no contribution to min_dist */ | ||
726 | if (x <= centerc1) { | ||
727 | tdist = (x - maxc1) * C1_SCALE; | ||
728 | max_dist += tdist*tdist; | ||
729 | } else { | ||
730 | tdist = (x - minc1) * C1_SCALE; | ||
731 | max_dist += tdist*tdist; | ||
732 | } | ||
733 | } | ||
734 | |||
735 | x = GETJSAMPLE(cinfo->colormap[2][i]); | ||
736 | if (x < minc2) { | ||
737 | tdist = (x - minc2) * C2_SCALE; | ||
738 | min_dist += tdist*tdist; | ||
739 | tdist = (x - maxc2) * C2_SCALE; | ||
740 | max_dist += tdist*tdist; | ||
741 | } else if (x > maxc2) { | ||
742 | tdist = (x - maxc2) * C2_SCALE; | ||
743 | min_dist += tdist*tdist; | ||
744 | tdist = (x - minc2) * C2_SCALE; | ||
745 | max_dist += tdist*tdist; | ||
746 | } else { | ||
747 | /* within cell range so no contribution to min_dist */ | ||
748 | if (x <= centerc2) { | ||
749 | tdist = (x - maxc2) * C2_SCALE; | ||
750 | max_dist += tdist*tdist; | ||
751 | } else { | ||
752 | tdist = (x - minc2) * C2_SCALE; | ||
753 | max_dist += tdist*tdist; | ||
754 | } | ||
755 | } | ||
756 | |||
757 | mindist[i] = min_dist; /* save away the results */ | ||
758 | if (max_dist < minmaxdist) | ||
759 | minmaxdist = max_dist; | ||
760 | } | ||
761 | |||
762 | /* Now we know that no cell in the update box is more than minmaxdist | ||
763 | * away from some colormap entry. Therefore, only colors that are | ||
764 | * within minmaxdist of some part of the box need be considered. | ||
765 | */ | ||
766 | ncolors = 0; | ||
767 | for (i = 0; i < numcolors; i++) { | ||
768 | if (mindist[i] <= minmaxdist) | ||
769 | colorlist[ncolors++] = (JSAMPLE) i; | ||
770 | } | ||
771 | return ncolors; | ||
772 | } | ||
773 | |||
774 | |||
775 | LOCAL(void) | ||
776 | find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, | ||
777 | int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[]) | ||
778 | /* Find the closest colormap entry for each cell in the update box, | ||
779 | * given the list of candidate colors prepared by find_nearby_colors. | ||
780 | * Return the indexes of the closest entries in the bestcolor[] array. | ||
781 | * This routine uses Thomas' incremental distance calculation method to | ||
782 | * find the distance from a colormap entry to successive cells in the box. | ||
783 | */ | ||
784 | { | ||
785 | int ic0, ic1, ic2; | ||
786 | int i, icolor; | ||
787 | register INT32 * bptr; /* pointer into bestdist[] array */ | ||
788 | JSAMPLE * cptr; /* pointer into bestcolor[] array */ | ||
789 | INT32 dist0, dist1; /* initial distance values */ | ||
790 | register INT32 dist2; /* current distance in inner loop */ | ||
791 | INT32 xx0, xx1; /* distance increments */ | ||
792 | register INT32 xx2; | ||
793 | INT32 inc0, inc1, inc2; /* initial values for increments */ | ||
794 | /* This array holds the distance to the nearest-so-far color for each cell */ | ||
795 | INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; | ||
796 | |||
797 | /* Initialize best-distance for each cell of the update box */ | ||
798 | bptr = bestdist; | ||
799 | for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--) | ||
800 | *bptr++ = 0x7FFFFFFFL; | ||
801 | |||
802 | /* For each color selected by find_nearby_colors, | ||
803 | * compute its distance to the center of each cell in the box. | ||
804 | * If that's less than best-so-far, update best distance and color number. | ||
805 | */ | ||
806 | |||
807 | /* Nominal steps between cell centers ("x" in Thomas article) */ | ||
808 | #define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) | ||
809 | #define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) | ||
810 | #define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) | ||
811 | |||
812 | for (i = 0; i < numcolors; i++) { | ||
813 | icolor = GETJSAMPLE(colorlist[i]); | ||
814 | /* Compute (square of) distance from minc0/c1/c2 to this color */ | ||
815 | inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE; | ||
816 | dist0 = inc0*inc0; | ||
817 | inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE; | ||
818 | dist0 += inc1*inc1; | ||
819 | inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE; | ||
820 | dist0 += inc2*inc2; | ||
821 | /* Form the initial difference increments */ | ||
822 | inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; | ||
823 | inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; | ||
824 | inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; | ||
825 | /* Now loop over all cells in box, updating distance per Thomas method */ | ||
826 | bptr = bestdist; | ||
827 | cptr = bestcolor; | ||
828 | xx0 = inc0; | ||
829 | for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) { | ||
830 | dist1 = dist0; | ||
831 | xx1 = inc1; | ||
832 | for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) { | ||
833 | dist2 = dist1; | ||
834 | xx2 = inc2; | ||
835 | for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) { | ||
836 | if (dist2 < *bptr) { | ||
837 | *bptr = dist2; | ||
838 | *cptr = (JSAMPLE) icolor; | ||
839 | } | ||
840 | dist2 += xx2; | ||
841 | xx2 += 2 * STEP_C2 * STEP_C2; | ||
842 | bptr++; | ||
843 | cptr++; | ||
844 | } | ||
845 | dist1 += xx1; | ||
846 | xx1 += 2 * STEP_C1 * STEP_C1; | ||
847 | } | ||
848 | dist0 += xx0; | ||
849 | xx0 += 2 * STEP_C0 * STEP_C0; | ||
850 | } | ||
851 | } | ||
852 | } | ||
853 | |||
854 | |||
855 | LOCAL(void) | ||
856 | fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2) | ||
857 | /* Fill the inverse-colormap entries in the update box that contains */ | ||
858 | /* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */ | ||
859 | /* we can fill as many others as we wish.) */ | ||
860 | { | ||
861 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
862 | hist3d histogram = cquantize->histogram; | ||
863 | int minc0, minc1, minc2; /* lower left corner of update box */ | ||
864 | int ic0, ic1, ic2; | ||
865 | register JSAMPLE * cptr; /* pointer into bestcolor[] array */ | ||
866 | register histptr cachep; /* pointer into main cache array */ | ||
867 | /* This array lists the candidate colormap indexes. */ | ||
868 | JSAMPLE colorlist[MAXNUMCOLORS]; | ||
869 | int numcolors; /* number of candidate colors */ | ||
870 | /* This array holds the actually closest colormap index for each cell. */ | ||
871 | JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; | ||
872 | |||
873 | /* Convert cell coordinates to update box ID */ | ||
874 | c0 >>= BOX_C0_LOG; | ||
875 | c1 >>= BOX_C1_LOG; | ||
876 | c2 >>= BOX_C2_LOG; | ||
877 | |||
878 | /* Compute true coordinates of update box's origin corner. | ||
879 | * Actually we compute the coordinates of the center of the corner | ||
880 | * histogram cell, which are the lower bounds of the volume we care about. | ||
881 | */ | ||
882 | minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); | ||
883 | minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); | ||
884 | minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); | ||
885 | |||
886 | /* Determine which colormap entries are close enough to be candidates | ||
887 | * for the nearest entry to some cell in the update box. | ||
888 | */ | ||
889 | numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist); | ||
890 | |||
891 | /* Determine the actually nearest colors. */ | ||
892 | find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist, | ||
893 | bestcolor); | ||
894 | |||
895 | /* Save the best color numbers (plus 1) in the main cache array */ | ||
896 | c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */ | ||
897 | c1 <<= BOX_C1_LOG; | ||
898 | c2 <<= BOX_C2_LOG; | ||
899 | cptr = bestcolor; | ||
900 | for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) { | ||
901 | for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) { | ||
902 | cachep = & histogram[c0+ic0][c1+ic1][c2]; | ||
903 | for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) { | ||
904 | *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); | ||
905 | } | ||
906 | } | ||
907 | } | ||
908 | } | ||
909 | |||
910 | |||
911 | /* | ||
912 | * Map some rows of pixels to the output colormapped representation. | ||
913 | */ | ||
914 | |||
915 | METHODDEF(void) | ||
916 | pass2_no_dither (j_decompress_ptr cinfo, | ||
917 | JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) | ||
918 | /* This version performs no dithering */ | ||
919 | { | ||
920 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
921 | hist3d histogram = cquantize->histogram; | ||
922 | register JSAMPROW inptr, outptr; | ||
923 | register histptr cachep; | ||
924 | register int c0, c1, c2; | ||
925 | int row; | ||
926 | JDIMENSION col; | ||
927 | JDIMENSION width = cinfo->output_width; | ||
928 | |||
929 | for (row = 0; row < num_rows; row++) { | ||
930 | inptr = input_buf[row]; | ||
931 | outptr = output_buf[row]; | ||
932 | for (col = width; col > 0; col--) { | ||
933 | /* get pixel value and index into the cache */ | ||
934 | c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT; | ||
935 | c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT; | ||
936 | c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT; | ||
937 | cachep = & histogram[c0][c1][c2]; | ||
938 | /* If we have not seen this color before, find nearest colormap entry */ | ||
939 | /* and update the cache */ | ||
940 | if (*cachep == 0) | ||
941 | fill_inverse_cmap(cinfo, c0,c1,c2); | ||
942 | /* Now emit the colormap index for this cell */ | ||
943 | *outptr++ = (JSAMPLE) (*cachep - 1); | ||
944 | } | ||
945 | } | ||
946 | } | ||
947 | |||
948 | |||
949 | METHODDEF(void) | ||
950 | pass2_fs_dither (j_decompress_ptr cinfo, | ||
951 | JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) | ||
952 | /* This version performs Floyd-Steinberg dithering */ | ||
953 | { | ||
954 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
955 | hist3d histogram = cquantize->histogram; | ||
956 | register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */ | ||
957 | LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */ | ||
958 | LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */ | ||
959 | register FSERRPTR errorptr; /* => fserrors[] at column before current */ | ||
960 | JSAMPROW inptr; /* => current input pixel */ | ||
961 | JSAMPROW outptr; /* => current output pixel */ | ||
962 | histptr cachep; | ||
963 | int dir; /* +1 or -1 depending on direction */ | ||
964 | int dir3; /* 3*dir, for advancing inptr & errorptr */ | ||
965 | int row; | ||
966 | JDIMENSION col; | ||
967 | JDIMENSION width = cinfo->output_width; | ||
968 | JSAMPLE *range_limit = cinfo->sample_range_limit; | ||
969 | int *error_limit = cquantize->error_limiter; | ||
970 | JSAMPROW colormap0 = cinfo->colormap[0]; | ||
971 | JSAMPROW colormap1 = cinfo->colormap[1]; | ||
972 | JSAMPROW colormap2 = cinfo->colormap[2]; | ||
973 | SHIFT_TEMPS | ||
974 | |||
975 | for (row = 0; row < num_rows; row++) { | ||
976 | inptr = input_buf[row]; | ||
977 | outptr = output_buf[row]; | ||
978 | if (cquantize->on_odd_row) { | ||
979 | /* work right to left in this row */ | ||
980 | inptr += (width-1) * 3; /* so point to rightmost pixel */ | ||
981 | outptr += width-1; | ||
982 | dir = -1; | ||
983 | dir3 = -3; | ||
984 | errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */ | ||
985 | cquantize->on_odd_row = FALSE; /* flip for next time */ | ||
986 | } else { | ||
987 | /* work left to right in this row */ | ||
988 | dir = 1; | ||
989 | dir3 = 3; | ||
990 | errorptr = cquantize->fserrors; /* => entry before first real column */ | ||
991 | cquantize->on_odd_row = TRUE; /* flip for next time */ | ||
992 | } | ||
993 | /* Preset error values: no error propagated to first pixel from left */ | ||
994 | cur0 = cur1 = cur2 = 0; | ||
995 | /* and no error propagated to row below yet */ | ||
996 | belowerr0 = belowerr1 = belowerr2 = 0; | ||
997 | bpreverr0 = bpreverr1 = bpreverr2 = 0; | ||
998 | |||
999 | for (col = width; col > 0; col--) { | ||
1000 | /* curN holds the error propagated from the previous pixel on the | ||
1001 | * current line. Add the error propagated from the previous line | ||
1002 | * to form the complete error correction term for this pixel, and | ||
1003 | * round the error term (which is expressed * 16) to an integer. | ||
1004 | * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct | ||
1005 | * for either sign of the error value. | ||
1006 | * Note: errorptr points to *previous* column's array entry. | ||
1007 | */ | ||
1008 | cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4); | ||
1009 | cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4); | ||
1010 | cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4); | ||
1011 | /* Limit the error using transfer function set by init_error_limit. | ||
1012 | * See comments with init_error_limit for rationale. | ||
1013 | */ | ||
1014 | cur0 = error_limit[cur0]; | ||
1015 | cur1 = error_limit[cur1]; | ||
1016 | cur2 = error_limit[cur2]; | ||
1017 | /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE. | ||
1018 | * The maximum error is +- MAXJSAMPLE (or less with error limiting); | ||
1019 | * this sets the required size of the range_limit array. | ||
1020 | */ | ||
1021 | cur0 += GETJSAMPLE(inptr[0]); | ||
1022 | cur1 += GETJSAMPLE(inptr[1]); | ||
1023 | cur2 += GETJSAMPLE(inptr[2]); | ||
1024 | cur0 = GETJSAMPLE(range_limit[cur0]); | ||
1025 | cur1 = GETJSAMPLE(range_limit[cur1]); | ||
1026 | cur2 = GETJSAMPLE(range_limit[cur2]); | ||
1027 | /* Index into the cache with adjusted pixel value */ | ||
1028 | cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT]; | ||
1029 | /* If we have not seen this color before, find nearest colormap */ | ||
1030 | /* entry and update the cache */ | ||
1031 | if (*cachep == 0) | ||
1032 | fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT); | ||
1033 | /* Now emit the colormap index for this cell */ | ||
1034 | { register int pixcode = *cachep - 1; | ||
1035 | *outptr = (JSAMPLE) pixcode; | ||
1036 | /* Compute representation error for this pixel */ | ||
1037 | cur0 -= GETJSAMPLE(colormap0[pixcode]); | ||
1038 | cur1 -= GETJSAMPLE(colormap1[pixcode]); | ||
1039 | cur2 -= GETJSAMPLE(colormap2[pixcode]); | ||
1040 | } | ||
1041 | /* Compute error fractions to be propagated to adjacent pixels. | ||
1042 | * Add these into the running sums, and simultaneously shift the | ||
1043 | * next-line error sums left by 1 column. | ||
1044 | */ | ||
1045 | { register LOCFSERROR bnexterr, delta; | ||
1046 | |||
1047 | bnexterr = cur0; /* Process component 0 */ | ||
1048 | delta = cur0 * 2; | ||
1049 | cur0 += delta; /* form error * 3 */ | ||
1050 | errorptr[0] = (FSERROR) (bpreverr0 + cur0); | ||
1051 | cur0 += delta; /* form error * 5 */ | ||
1052 | bpreverr0 = belowerr0 + cur0; | ||
1053 | belowerr0 = bnexterr; | ||
1054 | cur0 += delta; /* form error * 7 */ | ||
1055 | bnexterr = cur1; /* Process component 1 */ | ||
1056 | delta = cur1 * 2; | ||
1057 | cur1 += delta; /* form error * 3 */ | ||
1058 | errorptr[1] = (FSERROR) (bpreverr1 + cur1); | ||
1059 | cur1 += delta; /* form error * 5 */ | ||
1060 | bpreverr1 = belowerr1 + cur1; | ||
1061 | belowerr1 = bnexterr; | ||
1062 | cur1 += delta; /* form error * 7 */ | ||
1063 | bnexterr = cur2; /* Process component 2 */ | ||
1064 | delta = cur2 * 2; | ||
1065 | cur2 += delta; /* form error * 3 */ | ||
1066 | errorptr[2] = (FSERROR) (bpreverr2 + cur2); | ||
1067 | cur2 += delta; /* form error * 5 */ | ||
1068 | bpreverr2 = belowerr2 + cur2; | ||
1069 | belowerr2 = bnexterr; | ||
1070 | cur2 += delta; /* form error * 7 */ | ||
1071 | } | ||
1072 | /* At this point curN contains the 7/16 error value to be propagated | ||
1073 | * to the next pixel on the current line, and all the errors for the | ||
1074 | * next line have been shifted over. We are therefore ready to move on. | ||
1075 | */ | ||
1076 | inptr += dir3; /* Advance pixel pointers to next column */ | ||
1077 | outptr += dir; | ||
1078 | errorptr += dir3; /* advance errorptr to current column */ | ||
1079 | } | ||
1080 | /* Post-loop cleanup: we must unload the final error values into the | ||
1081 | * final fserrors[] entry. Note we need not unload belowerrN because | ||
1082 | * it is for the dummy column before or after the actual array. | ||
1083 | */ | ||
1084 | errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */ | ||
1085 | errorptr[1] = (FSERROR) bpreverr1; | ||
1086 | errorptr[2] = (FSERROR) bpreverr2; | ||
1087 | } | ||
1088 | } | ||
1089 | |||
1090 | |||
1091 | /* | ||
1092 | * Initialize the error-limiting transfer function (lookup table). | ||
1093 | * The raw F-S error computation can potentially compute error values of up to | ||
1094 | * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be | ||
1095 | * much less, otherwise obviously wrong pixels will be created. (Typical | ||
1096 | * effects include weird fringes at color-area boundaries, isolated bright | ||
1097 | * pixels in a dark area, etc.) The standard advice for avoiding this problem | ||
1098 | * is to ensure that the "corners" of the color cube are allocated as output | ||
1099 | * colors; then repeated errors in the same direction cannot cause cascading | ||
1100 | * error buildup. However, that only prevents the error from getting | ||
1101 | * completely out of hand; Aaron Giles reports that error limiting improves | ||
1102 | * the results even with corner colors allocated. | ||
1103 | * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty | ||
1104 | * well, but the smoother transfer function used below is even better. Thanks | ||
1105 | * to Aaron Giles for this idea. | ||
1106 | */ | ||
1107 | |||
1108 | LOCAL(void) | ||
1109 | init_error_limit (j_decompress_ptr cinfo) | ||
1110 | /* Allocate and fill in the error_limiter table */ | ||
1111 | { | ||
1112 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
1113 | int * table; | ||
1114 | int in, out; | ||
1115 | |||
1116 | table = (int *) (*cinfo->mem->alloc_small) | ||
1117 | ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int)); | ||
1118 | table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */ | ||
1119 | cquantize->error_limiter = table; | ||
1120 | |||
1121 | #define STEPSIZE ((MAXJSAMPLE+1)/16) | ||
1122 | /* Map errors 1:1 up to +- MAXJSAMPLE/16 */ | ||
1123 | out = 0; | ||
1124 | for (in = 0; in < STEPSIZE; in++, out++) { | ||
1125 | table[in] = out; table[-in] = -out; | ||
1126 | } | ||
1127 | /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */ | ||
1128 | for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) { | ||
1129 | table[in] = out; table[-in] = -out; | ||
1130 | } | ||
1131 | /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */ | ||
1132 | for (; in <= MAXJSAMPLE; in++) { | ||
1133 | table[in] = out; table[-in] = -out; | ||
1134 | } | ||
1135 | #undef STEPSIZE | ||
1136 | } | ||
1137 | |||
1138 | |||
1139 | /* | ||
1140 | * Finish up at the end of each pass. | ||
1141 | */ | ||
1142 | |||
1143 | METHODDEF(void) | ||
1144 | finish_pass1 (j_decompress_ptr cinfo) | ||
1145 | { | ||
1146 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
1147 | |||
1148 | /* Select the representative colors and fill in cinfo->colormap */ | ||
1149 | cinfo->colormap = cquantize->sv_colormap; | ||
1150 | select_colors(cinfo, cquantize->desired); | ||
1151 | /* Force next pass to zero the color index table */ | ||
1152 | cquantize->needs_zeroed = TRUE; | ||
1153 | } | ||
1154 | |||
1155 | |||
1156 | METHODDEF(void) | ||
1157 | finish_pass2 (j_decompress_ptr cinfo) | ||
1158 | { | ||
1159 | /* no work */ | ||
1160 | } | ||
1161 | |||
1162 | |||
1163 | /* | ||
1164 | * Initialize for each processing pass. | ||
1165 | */ | ||
1166 | |||
1167 | METHODDEF(void) | ||
1168 | start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan) | ||
1169 | { | ||
1170 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
1171 | hist3d histogram = cquantize->histogram; | ||
1172 | int i; | ||
1173 | |||
1174 | /* Only F-S dithering or no dithering is supported. */ | ||
1175 | /* If user asks for ordered dither, give him F-S. */ | ||
1176 | if (cinfo->dither_mode != JDITHER_NONE) | ||
1177 | cinfo->dither_mode = JDITHER_FS; | ||
1178 | |||
1179 | if (is_pre_scan) { | ||
1180 | /* Set up method pointers */ | ||
1181 | cquantize->pub.color_quantize = prescan_quantize; | ||
1182 | cquantize->pub.finish_pass = finish_pass1; | ||
1183 | cquantize->needs_zeroed = TRUE; /* Always zero histogram */ | ||
1184 | } else { | ||
1185 | /* Set up method pointers */ | ||
1186 | if (cinfo->dither_mode == JDITHER_FS) | ||
1187 | cquantize->pub.color_quantize = pass2_fs_dither; | ||
1188 | else | ||
1189 | cquantize->pub.color_quantize = pass2_no_dither; | ||
1190 | cquantize->pub.finish_pass = finish_pass2; | ||
1191 | |||
1192 | /* Make sure color count is acceptable */ | ||
1193 | i = cinfo->actual_number_of_colors; | ||
1194 | if (i < 1) | ||
1195 | ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1); | ||
1196 | if (i > MAXNUMCOLORS) | ||
1197 | ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); | ||
1198 | |||
1199 | if (cinfo->dither_mode == JDITHER_FS) { | ||
1200 | size_t arraysize = (size_t) ((cinfo->output_width + 2) * | ||
1201 | (3 * SIZEOF(FSERROR))); | ||
1202 | /* Allocate Floyd-Steinberg workspace if we didn't already. */ | ||
1203 | if (cquantize->fserrors == NULL) | ||
1204 | cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) | ||
1205 | ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize); | ||
1206 | /* Initialize the propagated errors to zero. */ | ||
1207 | FMEMZERO((void FAR *) cquantize->fserrors, arraysize); | ||
1208 | /* Make the error-limit table if we didn't already. */ | ||
1209 | if (cquantize->error_limiter == NULL) | ||
1210 | init_error_limit(cinfo); | ||
1211 | cquantize->on_odd_row = FALSE; | ||
1212 | } | ||
1213 | |||
1214 | } | ||
1215 | /* Zero the histogram or inverse color map, if necessary */ | ||
1216 | if (cquantize->needs_zeroed) { | ||
1217 | for (i = 0; i < HIST_C0_ELEMS; i++) { | ||
1218 | FMEMZERO((void FAR *) histogram[i], | ||
1219 | HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); | ||
1220 | } | ||
1221 | cquantize->needs_zeroed = FALSE; | ||
1222 | } | ||
1223 | } | ||
1224 | |||
1225 | |||
1226 | /* | ||
1227 | * Switch to a new external colormap between output passes. | ||
1228 | */ | ||
1229 | |||
1230 | METHODDEF(void) | ||
1231 | new_color_map_2_quant (j_decompress_ptr cinfo) | ||
1232 | { | ||
1233 | my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | ||
1234 | |||
1235 | /* Reset the inverse color map */ | ||
1236 | cquantize->needs_zeroed = TRUE; | ||
1237 | } | ||
1238 | |||
1239 | |||
1240 | /* | ||
1241 | * Module initialization routine for 2-pass color quantization. | ||
1242 | */ | ||
1243 | |||
1244 | GLOBAL(void) | ||
1245 | jinit_2pass_quantizer (j_decompress_ptr cinfo) | ||
1246 | { | ||
1247 | my_cquantize_ptr cquantize; | ||
1248 | int i; | ||
1249 | |||
1250 | cquantize = (my_cquantize_ptr) | ||
1251 | (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, | ||
1252 | SIZEOF(my_cquantizer)); | ||
1253 | cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; | ||
1254 | cquantize->pub.start_pass = start_pass_2_quant; | ||
1255 | cquantize->pub.new_color_map = new_color_map_2_quant; | ||
1256 | cquantize->fserrors = NULL; /* flag optional arrays not allocated */ | ||
1257 | cquantize->error_limiter = NULL; | ||
1258 | |||
1259 | /* Make sure jdmaster didn't give me a case I can't handle */ | ||
1260 | if (cinfo->out_color_components != 3) | ||
1261 | ERREXIT(cinfo, JERR_NOTIMPL); | ||
1262 | |||
1263 | /* Allocate the histogram/inverse colormap storage */ | ||
1264 | cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small) | ||
1265 | ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d)); | ||
1266 | for (i = 0; i < HIST_C0_ELEMS; i++) { | ||
1267 | cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large) | ||
1268 | ((j_common_ptr) cinfo, JPOOL_IMAGE, | ||
1269 | HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); | ||
1270 | } | ||
1271 | cquantize->needs_zeroed = TRUE; /* histogram is garbage now */ | ||
1272 | |||
1273 | /* Allocate storage for the completed colormap, if required. | ||
1274 | * We do this now since it is FAR storage and may affect | ||
1275 | * the memory manager's space calculations. | ||
1276 | */ | ||
1277 | if (cinfo->enable_2pass_quant) { | ||
1278 | /* Make sure color count is acceptable */ | ||
1279 | int desired = cinfo->desired_number_of_colors; | ||
1280 | /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */ | ||
1281 | if (desired < 8) | ||
1282 | ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8); | ||
1283 | /* Make sure colormap indexes can be represented by JSAMPLEs */ | ||
1284 | if (desired > MAXNUMCOLORS) | ||
1285 | ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); | ||
1286 | cquantize->sv_colormap = (*cinfo->mem->alloc_sarray) | ||
1287 | ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3); | ||
1288 | cquantize->desired = desired; | ||
1289 | } else | ||
1290 | cquantize->sv_colormap = NULL; | ||
1291 | |||
1292 | /* Only F-S dithering or no dithering is supported. */ | ||
1293 | /* If user asks for ordered dither, give him F-S. */ | ||
1294 | if (cinfo->dither_mode != JDITHER_NONE) | ||
1295 | cinfo->dither_mode = JDITHER_FS; | ||
1296 | |||
1297 | /* Allocate Floyd-Steinberg workspace if necessary. | ||
1298 | * This isn't really needed until pass 2, but again it is FAR storage. | ||
1299 | * Although we will cope with a later change in dither_mode, | ||
1300 | * we do not promise to honor max_memory_to_use if dither_mode changes. | ||
1301 | */ | ||
1302 | if (cinfo->dither_mode == JDITHER_FS) { | ||
1303 | cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) | ||
1304 | ((j_common_ptr) cinfo, JPOOL_IMAGE, | ||
1305 | (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR)))); | ||
1306 | /* Might as well create the error-limiting table too. */ | ||
1307 | init_error_limit(cinfo); | ||
1308 | } | ||
1309 | } | ||
1310 | |||
1311 | #endif /* QUANT_2PASS_SUPPORTED */ | ||