1 /* 2 * jquant2.c 3 * 4 * This file was part of the Independent JPEG Group's software: 5 * Copyright (C) 1991-1996, Thomas G. Lane. 6 * libjpeg-turbo Modifications: 7 * Copyright (C) 2009, 2014, D. R. Commander. 8 * For conditions of distribution and use, see the accompanying README file. 9 * 10 * This file contains 2-pass color quantization (color mapping) routines. 11 * These routines provide selection of a custom color map for an image, 12 * followed by mapping of the image to that color map, with optional 13 * Floyd-Steinberg dithering. 14 * It is also possible to use just the second pass to map to an arbitrary 15 * externally-given color map. 16 * 17 * Note: ordered dithering is not supported, since there isn't any fast 18 * way to compute intercolor distances; it's unclear that ordered dither's 19 * fundamental assumptions even hold with an irregularly spaced color map. 20 */ 21 22 #define JPEG_INTERNALS 23 #include "jinclude.h" 24 #include "jpeglib.h" 25 26 #ifdef QUANT_2PASS_SUPPORTED 27 28 29 /* 30 * This module implements the well-known Heckbert paradigm for color 31 * quantization. Most of the ideas used here can be traced back to 32 * Heckbert's seminal paper 33 * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display", 34 * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304. 35 * 36 * In the first pass over the image, we accumulate a histogram showing the 37 * usage count of each possible color. To keep the histogram to a reasonable 38 * size, we reduce the precision of the input; typical practice is to retain 39 * 5 or 6 bits per color, so that 8 or 4 different input values are counted 40 * in the same histogram cell. 41 * 42 * Next, the color-selection step begins with a box representing the whole 43 * color space, and repeatedly splits the "largest" remaining box until we 44 * have as many boxes as desired colors. Then the mean color in each 45 * remaining box becomes one of the possible output colors. 46 * 47 * The second pass over the image maps each input pixel to the closest output 48 * color (optionally after applying a Floyd-Steinberg dithering correction). 49 * This mapping is logically trivial, but making it go fast enough requires 50 * considerable care. 51 * 52 * Heckbert-style quantizers vary a good deal in their policies for choosing 53 * the "largest" box and deciding where to cut it. The particular policies 54 * used here have proved out well in experimental comparisons, but better ones 55 * may yet be found. 56 * 57 * In earlier versions of the IJG code, this module quantized in YCbCr color 58 * space, processing the raw upsampled data without a color conversion step. 59 * This allowed the color conversion math to be done only once per colormap 60 * entry, not once per pixel. However, that optimization precluded other 61 * useful optimizations (such as merging color conversion with upsampling) 62 * and it also interfered with desired capabilities such as quantizing to an 63 * externally-supplied colormap. We have therefore abandoned that approach. 64 * The present code works in the post-conversion color space, typically RGB. 65 * 66 * To improve the visual quality of the results, we actually work in scaled 67 * RGB space, giving G distances more weight than R, and R in turn more than 68 * B. To do everything in integer math, we must use integer scale factors. 69 * The 2/3/1 scale factors used here correspond loosely to the relative 70 * weights of the colors in the NTSC grayscale equation. 71 * If you want to use this code to quantize a non-RGB color space, you'll 72 * probably need to change these scale factors. 73 */ 74 75 #define R_SCALE 2 /* scale R distances by this much */ 76 #define G_SCALE 3 /* scale G distances by this much */ 77 #define B_SCALE 1 /* and B by this much */ 78 79 static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE}; 80 #define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]] 81 #define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]] 82 #define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]] 83 84 /* 85 * First we have the histogram data structure and routines for creating it. 86 * 87 * The number of bits of precision can be adjusted by changing these symbols. 88 * We recommend keeping 6 bits for G and 5 each for R and B. 89 * If you have plenty of memory and cycles, 6 bits all around gives marginally 90 * better results; if you are short of memory, 5 bits all around will save 91 * some space but degrade the results. 92 * To maintain a fully accurate histogram, we'd need to allocate a "long" 93 * (preferably unsigned long) for each cell. In practice this is overkill; 94 * we can get by with 16 bits per cell. Few of the cell counts will overflow, 95 * and clamping those that do overflow to the maximum value will give close- 96 * enough results. This reduces the recommended histogram size from 256Kb 97 * to 128Kb, which is a useful savings on PC-class machines. 98 * (In the second pass the histogram space is re-used for pixel mapping data; 99 * in that capacity, each cell must be able to store zero to the number of 100 * desired colors. 16 bits/cell is plenty for that too.) 101 * Since the JPEG code is intended to run in small memory model on 80x86 102 * machines, we can't just allocate the histogram in one chunk. Instead 103 * of a true 3-D array, we use a row of pointers to 2-D arrays. Each 104 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and 105 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. 106 */ 107 108 #define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */ 109 110 /* These will do the right thing for either R,G,B or B,G,R color order, 111 * but you may not like the results for other color orders. 112 */ 113 #define HIST_C0_BITS 5 /* bits of precision in R/B histogram */ 114 #define HIST_C1_BITS 6 /* bits of precision in G histogram */ 115 #define HIST_C2_BITS 5 /* bits of precision in B/R histogram */ 116 117 /* Number of elements along histogram axes. */ 118 #define HIST_C0_ELEMS (1<<HIST_C0_BITS) 119 #define HIST_C1_ELEMS (1<<HIST_C1_BITS) 120 #define HIST_C2_ELEMS (1<<HIST_C2_BITS) 121 122 /* These are the amounts to shift an input value to get a histogram index. */ 123 #define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS) 124 #define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS) 125 #define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS) 126 127 128 typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */ 129 130 typedef histcell * histptr; /* for pointers to histogram cells */ 131 132 typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */ 133 typedef hist1d * hist2d; /* type for the 2nd-level pointers */ 134 typedef hist2d * hist3d; /* type for top-level pointer */ 135 136 137 /* Declarations for Floyd-Steinberg dithering. 138 * 139 * Errors are accumulated into the array fserrors[], at a resolution of 140 * 1/16th of a pixel count. The error at a given pixel is propagated 141 * to its not-yet-processed neighbors using the standard F-S fractions, 142 * ... (here) 7/16 143 * 3/16 5/16 1/16 144 * We work left-to-right on even rows, right-to-left on odd rows. 145 * 146 * We can get away with a single array (holding one row's worth of errors) 147 * by using it to store the current row's errors at pixel columns not yet 148 * processed, but the next row's errors at columns already processed. We 149 * need only a few extra variables to hold the errors immediately around the 150 * current column. (If we are lucky, those variables are in registers, but 151 * even if not, they're probably cheaper to access than array elements are.) 152 * 153 * The fserrors[] array has (#columns + 2) entries; the extra entry at 154 * each end saves us from special-casing the first and last pixels. 155 * Each entry is three values long, one value for each color component. 156 */ 157 158 #if BITS_IN_JSAMPLE == 8 159 typedef INT16 FSERROR; /* 16 bits should be enough */ 160 typedef int LOCFSERROR; /* use 'int' for calculation temps */ 161 #else 162 typedef INT32 FSERROR; /* may need more than 16 bits */ 163 typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */ 164 #endif 165 166 typedef FSERROR *FSERRPTR; /* pointer to error array */ 167 168 169 /* Private subobject */ 170 171 typedef struct { 172 struct jpeg_color_quantizer pub; /* public fields */ 173 174 /* Space for the eventually created colormap is stashed here */ 175 JSAMPARRAY sv_colormap; /* colormap allocated at init time */ 176 int desired; /* desired # of colors = size of colormap */ 177 178 /* Variables for accumulating image statistics */ 179 hist3d histogram; /* pointer to the histogram */ 180 181 boolean needs_zeroed; /* TRUE if next pass must zero histogram */ 182 183 /* Variables for Floyd-Steinberg dithering */ 184 FSERRPTR fserrors; /* accumulated errors */ 185 boolean on_odd_row; /* flag to remember which row we are on */ 186 int * error_limiter; /* table for clamping the applied error */ 187 } my_cquantizer; 188 189 typedef my_cquantizer * my_cquantize_ptr; 190 191 192 /* 193 * Prescan some rows of pixels. 194 * In this module the prescan simply updates the histogram, which has been 195 * initialized to zeroes by start_pass. 196 * An output_buf parameter is required by the method signature, but no data 197 * is actually output (in fact the buffer controller is probably passing a 198 * NULL pointer). 199 */ 200 201 METHODDEF(void) 202 prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf, 203 JSAMPARRAY output_buf, int num_rows) 204 { 205 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 206 register JSAMPROW ptr; 207 register histptr histp; 208 register hist3d histogram = cquantize->histogram; 209 int row; 210 JDIMENSION col; 211 JDIMENSION width = cinfo->output_width; 212 213 for (row = 0; row < num_rows; row++) { 214 ptr = input_buf[row]; 215 for (col = width; col > 0; col--) { 216 /* get pixel value and index into the histogram */ 217 histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT] 218 [GETJSAMPLE(ptr[1]) >> C1_SHIFT] 219 [GETJSAMPLE(ptr[2]) >> C2_SHIFT]; 220 /* increment, check for overflow and undo increment if so. */ 221 if (++(*histp) <= 0) 222 (*histp)--; 223 ptr += 3; 224 } 225 } 226 } 227 228 229 /* 230 * Next we have the really interesting routines: selection of a colormap 231 * given the completed histogram. 232 * These routines work with a list of "boxes", each representing a rectangular 233 * subset of the input color space (to histogram precision). 234 */ 235 236 typedef struct { 237 /* The bounds of the box (inclusive); expressed as histogram indexes */ 238 int c0min, c0max; 239 int c1min, c1max; 240 int c2min, c2max; 241 /* The volume (actually 2-norm) of the box */ 242 INT32 volume; 243 /* The number of nonzero histogram cells within this box */ 244 long colorcount; 245 } box; 246 247 typedef box * boxptr; 248 249 250 LOCAL(boxptr) 251 find_biggest_color_pop (boxptr boxlist, int numboxes) 252 /* Find the splittable box with the largest color population */ 253 /* Returns NULL if no splittable boxes remain */ 254 { 255 register boxptr boxp; 256 register int i; 257 register long maxc = 0; 258 boxptr which = NULL; 259 260 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { 261 if (boxp->colorcount > maxc && boxp->volume > 0) { 262 which = boxp; 263 maxc = boxp->colorcount; 264 } 265 } 266 return which; 267 } 268 269 270 LOCAL(boxptr) 271 find_biggest_volume (boxptr boxlist, int numboxes) 272 /* Find the splittable box with the largest (scaled) volume */ 273 /* Returns NULL if no splittable boxes remain */ 274 { 275 register boxptr boxp; 276 register int i; 277 register INT32 maxv = 0; 278 boxptr which = NULL; 279 280 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { 281 if (boxp->volume > maxv) { 282 which = boxp; 283 maxv = boxp->volume; 284 } 285 } 286 return which; 287 } 288 289 290 LOCAL(void) 291 update_box (j_decompress_ptr cinfo, boxptr boxp) 292 /* Shrink the min/max bounds of a box to enclose only nonzero elements, */ 293 /* and recompute its volume and population */ 294 { 295 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 296 hist3d histogram = cquantize->histogram; 297 histptr histp; 298 int c0,c1,c2; 299 int c0min,c0max,c1min,c1max,c2min,c2max; 300 INT32 dist0,dist1,dist2; 301 long ccount; 302 303 c0min = boxp->c0min; c0max = boxp->c0max; 304 c1min = boxp->c1min; c1max = boxp->c1max; 305 c2min = boxp->c2min; c2max = boxp->c2max; 306 307 if (c0max > c0min) 308 for (c0 = c0min; c0 <= c0max; c0++) 309 for (c1 = c1min; c1 <= c1max; c1++) { 310 histp = & histogram[c0][c1][c2min]; 311 for (c2 = c2min; c2 <= c2max; c2++) 312 if (*histp++ != 0) { 313 boxp->c0min = c0min = c0; 314 goto have_c0min; 315 } 316 } 317 have_c0min: 318 if (c0max > c0min) 319 for (c0 = c0max; c0 >= c0min; c0--) 320 for (c1 = c1min; c1 <= c1max; c1++) { 321 histp = & histogram[c0][c1][c2min]; 322 for (c2 = c2min; c2 <= c2max; c2++) 323 if (*histp++ != 0) { 324 boxp->c0max = c0max = c0; 325 goto have_c0max; 326 } 327 } 328 have_c0max: 329 if (c1max > c1min) 330 for (c1 = c1min; c1 <= c1max; c1++) 331 for (c0 = c0min; c0 <= c0max; c0++) { 332 histp = & histogram[c0][c1][c2min]; 333 for (c2 = c2min; c2 <= c2max; c2++) 334 if (*histp++ != 0) { 335 boxp->c1min = c1min = c1; 336 goto have_c1min; 337 } 338 } 339 have_c1min: 340 if (c1max > c1min) 341 for (c1 = c1max; c1 >= c1min; c1--) 342 for (c0 = c0min; c0 <= c0max; c0++) { 343 histp = & histogram[c0][c1][c2min]; 344 for (c2 = c2min; c2 <= c2max; c2++) 345 if (*histp++ != 0) { 346 boxp->c1max = c1max = c1; 347 goto have_c1max; 348 } 349 } 350 have_c1max: 351 if (c2max > c2min) 352 for (c2 = c2min; c2 <= c2max; c2++) 353 for (c0 = c0min; c0 <= c0max; c0++) { 354 histp = & histogram[c0][c1min][c2]; 355 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) 356 if (*histp != 0) { 357 boxp->c2min = c2min = c2; 358 goto have_c2min; 359 } 360 } 361 have_c2min: 362 if (c2max > c2min) 363 for (c2 = c2max; c2 >= c2min; c2--) 364 for (c0 = c0min; c0 <= c0max; c0++) { 365 histp = & histogram[c0][c1min][c2]; 366 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) 367 if (*histp != 0) { 368 boxp->c2max = c2max = c2; 369 goto have_c2max; 370 } 371 } 372 have_c2max: 373 374 /* Update box volume. 375 * We use 2-norm rather than real volume here; this biases the method 376 * against making long narrow boxes, and it has the side benefit that 377 * a box is splittable iff norm > 0. 378 * Since the differences are expressed in histogram-cell units, 379 * we have to shift back to JSAMPLE units to get consistent distances; 380 * after which, we scale according to the selected distance scale factors. 381 */ 382 dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; 383 dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; 384 dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; 385 boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2; 386 387 /* Now scan remaining volume of box and compute population */ 388 ccount = 0; 389 for (c0 = c0min; c0 <= c0max; c0++) 390 for (c1 = c1min; c1 <= c1max; c1++) { 391 histp = & histogram[c0][c1][c2min]; 392 for (c2 = c2min; c2 <= c2max; c2++, histp++) 393 if (*histp != 0) { 394 ccount++; 395 } 396 } 397 boxp->colorcount = ccount; 398 } 399 400 401 LOCAL(int) 402 median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes, 403 int desired_colors) 404 /* Repeatedly select and split the largest box until we have enough boxes */ 405 { 406 int n,lb; 407 int c0,c1,c2,cmax; 408 register boxptr b1,b2; 409 410 while (numboxes < desired_colors) { 411 /* Select box to split. 412 * Current algorithm: by population for first half, then by volume. 413 */ 414 if (numboxes*2 <= desired_colors) { 415 b1 = find_biggest_color_pop(boxlist, numboxes); 416 } else { 417 b1 = find_biggest_volume(boxlist, numboxes); 418 } 419 if (b1 == NULL) /* no splittable boxes left! */ 420 break; 421 b2 = &boxlist[numboxes]; /* where new box will go */ 422 /* Copy the color bounds to the new box. */ 423 b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max; 424 b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min; 425 /* Choose which axis to split the box on. 426 * Current algorithm: longest scaled axis. 427 * See notes in update_box about scaling distances. 428 */ 429 c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE; 430 c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE; 431 c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE; 432 /* We want to break any ties in favor of green, then red, blue last. 433 * This code does the right thing for R,G,B or B,G,R color orders only. 434 */ 435 if (rgb_red[cinfo->out_color_space] == 0) { 436 cmax = c1; n = 1; 437 if (c0 > cmax) { cmax = c0; n = 0; } 438 if (c2 > cmax) { n = 2; } 439 } 440 else { 441 cmax = c1; n = 1; 442 if (c2 > cmax) { cmax = c2; n = 2; } 443 if (c0 > cmax) { n = 0; } 444 } 445 /* Choose split point along selected axis, and update box bounds. 446 * Current algorithm: split at halfway point. 447 * (Since the box has been shrunk to minimum volume, 448 * any split will produce two nonempty subboxes.) 449 * Note that lb value is max for lower box, so must be < old max. 450 */ 451 switch (n) { 452 case 0: 453 lb = (b1->c0max + b1->c0min) / 2; 454 b1->c0max = lb; 455 b2->c0min = lb+1; 456 break; 457 case 1: 458 lb = (b1->c1max + b1->c1min) / 2; 459 b1->c1max = lb; 460 b2->c1min = lb+1; 461 break; 462 case 2: 463 lb = (b1->c2max + b1->c2min) / 2; 464 b1->c2max = lb; 465 b2->c2min = lb+1; 466 break; 467 } 468 /* Update stats for boxes */ 469 update_box(cinfo, b1); 470 update_box(cinfo, b2); 471 numboxes++; 472 } 473 return numboxes; 474 } 475 476 477 LOCAL(void) 478 compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor) 479 /* Compute representative color for a box, put it in colormap[icolor] */ 480 { 481 /* Current algorithm: mean weighted by pixels (not colors) */ 482 /* Note it is important to get the rounding correct! */ 483 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 484 hist3d histogram = cquantize->histogram; 485 histptr histp; 486 int c0,c1,c2; 487 int c0min,c0max,c1min,c1max,c2min,c2max; 488 long count; 489 long total = 0; 490 long c0total = 0; 491 long c1total = 0; 492 long c2total = 0; 493 494 c0min = boxp->c0min; c0max = boxp->c0max; 495 c1min = boxp->c1min; c1max = boxp->c1max; 496 c2min = boxp->c2min; c2max = boxp->c2max; 497 498 for (c0 = c0min; c0 <= c0max; c0++) 499 for (c1 = c1min; c1 <= c1max; c1++) { 500 histp = & histogram[c0][c1][c2min]; 501 for (c2 = c2min; c2 <= c2max; c2++) { 502 if ((count = *histp++) != 0) { 503 total += count; 504 c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count; 505 c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count; 506 c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count; 507 } 508 } 509 } 510 511 cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); 512 cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); 513 cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); 514 } 515 516 517 LOCAL(void) 518 select_colors (j_decompress_ptr cinfo, int desired_colors) 519 /* Master routine for color selection */ 520 { 521 boxptr boxlist; 522 int numboxes; 523 int i; 524 525 /* Allocate workspace for box list */ 526 boxlist = (boxptr) (*cinfo->mem->alloc_small) 527 ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * sizeof(box)); 528 /* Initialize one box containing whole space */ 529 numboxes = 1; 530 boxlist[0].c0min = 0; 531 boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; 532 boxlist[0].c1min = 0; 533 boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; 534 boxlist[0].c2min = 0; 535 boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; 536 /* Shrink it to actually-used volume and set its statistics */ 537 update_box(cinfo, & boxlist[0]); 538 /* Perform median-cut to produce final box list */ 539 numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors); 540 /* Compute the representative color for each box, fill colormap */ 541 for (i = 0; i < numboxes; i++) 542 compute_color(cinfo, & boxlist[i], i); 543 cinfo->actual_number_of_colors = numboxes; 544 TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes); 545 } 546 547 548 /* 549 * These routines are concerned with the time-critical task of mapping input 550 * colors to the nearest color in the selected colormap. 551 * 552 * We re-use the histogram space as an "inverse color map", essentially a 553 * cache for the results of nearest-color searches. All colors within a 554 * histogram cell will be mapped to the same colormap entry, namely the one 555 * closest to the cell's center. This may not be quite the closest entry to 556 * the actual input color, but it's almost as good. A zero in the cache 557 * indicates we haven't found the nearest color for that cell yet; the array 558 * is cleared to zeroes before starting the mapping pass. When we find the 559 * nearest color for a cell, its colormap index plus one is recorded in the 560 * cache for future use. The pass2 scanning routines call fill_inverse_cmap 561 * when they need to use an unfilled entry in the cache. 562 * 563 * Our method of efficiently finding nearest colors is based on the "locally 564 * sorted search" idea described by Heckbert and on the incremental distance 565 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics 566 * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that 567 * the distances from a given colormap entry to each cell of the histogram can 568 * be computed quickly using an incremental method: the differences between 569 * distances to adjacent cells themselves differ by a constant. This allows a 570 * fairly fast implementation of the "brute force" approach of computing the 571 * distance from every colormap entry to every histogram cell. Unfortunately, 572 * it needs a work array to hold the best-distance-so-far for each histogram 573 * cell (because the inner loop has to be over cells, not colormap entries). 574 * The work array elements have to be INT32s, so the work array would need 575 * 256Kb at our recommended precision. This is not feasible in DOS machines. 576 * 577 * To get around these problems, we apply Thomas' method to compute the 578 * nearest colors for only the cells within a small subbox of the histogram. 579 * The work array need be only as big as the subbox, so the memory usage 580 * problem is solved. Furthermore, we need not fill subboxes that are never 581 * referenced in pass2; many images use only part of the color gamut, so a 582 * fair amount of work is saved. An additional advantage of this 583 * approach is that we can apply Heckbert's locality criterion to quickly 584 * eliminate colormap entries that are far away from the subbox; typically 585 * three-fourths of the colormap entries are rejected by Heckbert's criterion, 586 * and we need not compute their distances to individual cells in the subbox. 587 * The speed of this approach is heavily influenced by the subbox size: too 588 * small means too much overhead, too big loses because Heckbert's criterion 589 * can't eliminate as many colormap entries. Empirically the best subbox 590 * size seems to be about 1/512th of the histogram (1/8th in each direction). 591 * 592 * Thomas' article also describes a refined method which is asymptotically 593 * faster than the brute-force method, but it is also far more complex and 594 * cannot efficiently be applied to small subboxes. It is therefore not 595 * useful for programs intended to be portable to DOS machines. On machines 596 * with plenty of memory, filling the whole histogram in one shot with Thomas' 597 * refined method might be faster than the present code --- but then again, 598 * it might not be any faster, and it's certainly more complicated. 599 */ 600 601 602 /* log2(histogram cells in update box) for each axis; this can be adjusted */ 603 #define BOX_C0_LOG (HIST_C0_BITS-3) 604 #define BOX_C1_LOG (HIST_C1_BITS-3) 605 #define BOX_C2_LOG (HIST_C2_BITS-3) 606 607 #define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */ 608 #define BOX_C1_ELEMS (1<<BOX_C1_LOG) 609 #define BOX_C2_ELEMS (1<<BOX_C2_LOG) 610 611 #define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG) 612 #define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG) 613 #define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG) 614 615 616 /* 617 * The next three routines implement inverse colormap filling. They could 618 * all be folded into one big routine, but splitting them up this way saves 619 * some stack space (the mindist[] and bestdist[] arrays need not coexist) 620 * and may allow some compilers to produce better code by registerizing more 621 * inner-loop variables. 622 */ 623 624 LOCAL(int) 625 find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, 626 JSAMPLE colorlist[]) 627 /* Locate the colormap entries close enough to an update box to be candidates 628 * for the nearest entry to some cell(s) in the update box. The update box 629 * is specified by the center coordinates of its first cell. The number of 630 * candidate colormap entries is returned, and their colormap indexes are 631 * placed in colorlist[]. 632 * This routine uses Heckbert's "locally sorted search" criterion to select 633 * the colors that need further consideration. 634 */ 635 { 636 int numcolors = cinfo->actual_number_of_colors; 637 int maxc0, maxc1, maxc2; 638 int centerc0, centerc1, centerc2; 639 int i, x, ncolors; 640 INT32 minmaxdist, min_dist, max_dist, tdist; 641 INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */ 642 643 /* Compute true coordinates of update box's upper corner and center. 644 * Actually we compute the coordinates of the center of the upper-corner 645 * histogram cell, which are the upper bounds of the volume we care about. 646 * Note that since ">>" rounds down, the "center" values may be closer to 647 * min than to max; hence comparisons to them must be "<=", not "<". 648 */ 649 maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT)); 650 centerc0 = (minc0 + maxc0) >> 1; 651 maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT)); 652 centerc1 = (minc1 + maxc1) >> 1; 653 maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT)); 654 centerc2 = (minc2 + maxc2) >> 1; 655 656 /* For each color in colormap, find: 657 * 1. its minimum squared-distance to any point in the update box 658 * (zero if color is within update box); 659 * 2. its maximum squared-distance to any point in the update box. 660 * Both of these can be found by considering only the corners of the box. 661 * We save the minimum distance for each color in mindist[]; 662 * only the smallest maximum distance is of interest. 663 */ 664 minmaxdist = 0x7FFFFFFFL; 665 666 for (i = 0; i < numcolors; i++) { 667 /* We compute the squared-c0-distance term, then add in the other two. */ 668 x = GETJSAMPLE(cinfo->colormap[0][i]); 669 if (x < minc0) { 670 tdist = (x - minc0) * C0_SCALE; 671 min_dist = tdist*tdist; 672 tdist = (x - maxc0) * C0_SCALE; 673 max_dist = tdist*tdist; 674 } else if (x > maxc0) { 675 tdist = (x - maxc0) * C0_SCALE; 676 min_dist = tdist*tdist; 677 tdist = (x - minc0) * C0_SCALE; 678 max_dist = tdist*tdist; 679 } else { 680 /* within cell range so no contribution to min_dist */ 681 min_dist = 0; 682 if (x <= centerc0) { 683 tdist = (x - maxc0) * C0_SCALE; 684 max_dist = tdist*tdist; 685 } else { 686 tdist = (x - minc0) * C0_SCALE; 687 max_dist = tdist*tdist; 688 } 689 } 690 691 x = GETJSAMPLE(cinfo->colormap[1][i]); 692 if (x < minc1) { 693 tdist = (x - minc1) * C1_SCALE; 694 min_dist += tdist*tdist; 695 tdist = (x - maxc1) * C1_SCALE; 696 max_dist += tdist*tdist; 697 } else if (x > maxc1) { 698 tdist = (x - maxc1) * C1_SCALE; 699 min_dist += tdist*tdist; 700 tdist = (x - minc1) * C1_SCALE; 701 max_dist += tdist*tdist; 702 } else { 703 /* within cell range so no contribution to min_dist */ 704 if (x <= centerc1) { 705 tdist = (x - maxc1) * C1_SCALE; 706 max_dist += tdist*tdist; 707 } else { 708 tdist = (x - minc1) * C1_SCALE; 709 max_dist += tdist*tdist; 710 } 711 } 712 713 x = GETJSAMPLE(cinfo->colormap[2][i]); 714 if (x < minc2) { 715 tdist = (x - minc2) * C2_SCALE; 716 min_dist += tdist*tdist; 717 tdist = (x - maxc2) * C2_SCALE; 718 max_dist += tdist*tdist; 719 } else if (x > maxc2) { 720 tdist = (x - maxc2) * C2_SCALE; 721 min_dist += tdist*tdist; 722 tdist = (x - minc2) * C2_SCALE; 723 max_dist += tdist*tdist; 724 } else { 725 /* within cell range so no contribution to min_dist */ 726 if (x <= centerc2) { 727 tdist = (x - maxc2) * C2_SCALE; 728 max_dist += tdist*tdist; 729 } else { 730 tdist = (x - minc2) * C2_SCALE; 731 max_dist += tdist*tdist; 732 } 733 } 734 735 mindist[i] = min_dist; /* save away the results */ 736 if (max_dist < minmaxdist) 737 minmaxdist = max_dist; 738 } 739 740 /* Now we know that no cell in the update box is more than minmaxdist 741 * away from some colormap entry. Therefore, only colors that are 742 * within minmaxdist of some part of the box need be considered. 743 */ 744 ncolors = 0; 745 for (i = 0; i < numcolors; i++) { 746 if (mindist[i] <= minmaxdist) 747 colorlist[ncolors++] = (JSAMPLE) i; 748 } 749 return ncolors; 750 } 751 752 753 LOCAL(void) 754 find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, 755 int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[]) 756 /* Find the closest colormap entry for each cell in the update box, 757 * given the list of candidate colors prepared by find_nearby_colors. 758 * Return the indexes of the closest entries in the bestcolor[] array. 759 * This routine uses Thomas' incremental distance calculation method to 760 * find the distance from a colormap entry to successive cells in the box. 761 */ 762 { 763 int ic0, ic1, ic2; 764 int i, icolor; 765 register INT32 * bptr; /* pointer into bestdist[] array */ 766 JSAMPLE * cptr; /* pointer into bestcolor[] array */ 767 INT32 dist0, dist1; /* initial distance values */ 768 register INT32 dist2; /* current distance in inner loop */ 769 INT32 xx0, xx1; /* distance increments */ 770 register INT32 xx2; 771 INT32 inc0, inc1, inc2; /* initial values for increments */ 772 /* This array holds the distance to the nearest-so-far color for each cell */ 773 INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; 774 775 /* Initialize best-distance for each cell of the update box */ 776 bptr = bestdist; 777 for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--) 778 *bptr++ = 0x7FFFFFFFL; 779 780 /* For each color selected by find_nearby_colors, 781 * compute its distance to the center of each cell in the box. 782 * If that's less than best-so-far, update best distance and color number. 783 */ 784 785 /* Nominal steps between cell centers ("x" in Thomas article) */ 786 #define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) 787 #define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) 788 #define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) 789 790 for (i = 0; i < numcolors; i++) { 791 icolor = GETJSAMPLE(colorlist[i]); 792 /* Compute (square of) distance from minc0/c1/c2 to this color */ 793 inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE; 794 dist0 = inc0*inc0; 795 inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE; 796 dist0 += inc1*inc1; 797 inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE; 798 dist0 += inc2*inc2; 799 /* Form the initial difference increments */ 800 inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; 801 inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; 802 inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; 803 /* Now loop over all cells in box, updating distance per Thomas method */ 804 bptr = bestdist; 805 cptr = bestcolor; 806 xx0 = inc0; 807 for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) { 808 dist1 = dist0; 809 xx1 = inc1; 810 for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) { 811 dist2 = dist1; 812 xx2 = inc2; 813 for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) { 814 if (dist2 < *bptr) { 815 *bptr = dist2; 816 *cptr = (JSAMPLE) icolor; 817 } 818 dist2 += xx2; 819 xx2 += 2 * STEP_C2 * STEP_C2; 820 bptr++; 821 cptr++; 822 } 823 dist1 += xx1; 824 xx1 += 2 * STEP_C1 * STEP_C1; 825 } 826 dist0 += xx0; 827 xx0 += 2 * STEP_C0 * STEP_C0; 828 } 829 } 830 } 831 832 833 LOCAL(void) 834 fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2) 835 /* Fill the inverse-colormap entries in the update box that contains */ 836 /* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */ 837 /* we can fill as many others as we wish.) */ 838 { 839 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 840 hist3d histogram = cquantize->histogram; 841 int minc0, minc1, minc2; /* lower left corner of update box */ 842 int ic0, ic1, ic2; 843 register JSAMPLE * cptr; /* pointer into bestcolor[] array */ 844 register histptr cachep; /* pointer into main cache array */ 845 /* This array lists the candidate colormap indexes. */ 846 JSAMPLE colorlist[MAXNUMCOLORS]; 847 int numcolors; /* number of candidate colors */ 848 /* This array holds the actually closest colormap index for each cell. */ 849 JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; 850 851 /* Convert cell coordinates to update box ID */ 852 c0 >>= BOX_C0_LOG; 853 c1 >>= BOX_C1_LOG; 854 c2 >>= BOX_C2_LOG; 855 856 /* Compute true coordinates of update box's origin corner. 857 * Actually we compute the coordinates of the center of the corner 858 * histogram cell, which are the lower bounds of the volume we care about. 859 */ 860 minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); 861 minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); 862 minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); 863 864 /* Determine which colormap entries are close enough to be candidates 865 * for the nearest entry to some cell in the update box. 866 */ 867 numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist); 868 869 /* Determine the actually nearest colors. */ 870 find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist, 871 bestcolor); 872 873 /* Save the best color numbers (plus 1) in the main cache array */ 874 c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */ 875 c1 <<= BOX_C1_LOG; 876 c2 <<= BOX_C2_LOG; 877 cptr = bestcolor; 878 for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) { 879 for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) { 880 cachep = & histogram[c0+ic0][c1+ic1][c2]; 881 for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) { 882 *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); 883 } 884 } 885 } 886 } 887 888 889 /* 890 * Map some rows of pixels to the output colormapped representation. 891 */ 892 893 METHODDEF(void) 894 pass2_no_dither (j_decompress_ptr cinfo, 895 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) 896 /* This version performs no dithering */ 897 { 898 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 899 hist3d histogram = cquantize->histogram; 900 register JSAMPROW inptr, outptr; 901 register histptr cachep; 902 register int c0, c1, c2; 903 int row; 904 JDIMENSION col; 905 JDIMENSION width = cinfo->output_width; 906 907 for (row = 0; row < num_rows; row++) { 908 inptr = input_buf[row]; 909 outptr = output_buf[row]; 910 for (col = width; col > 0; col--) { 911 /* get pixel value and index into the cache */ 912 c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT; 913 c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT; 914 c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT; 915 cachep = & histogram[c0][c1][c2]; 916 /* If we have not seen this color before, find nearest colormap entry */ 917 /* and update the cache */ 918 if (*cachep == 0) 919 fill_inverse_cmap(cinfo, c0,c1,c2); 920 /* Now emit the colormap index for this cell */ 921 *outptr++ = (JSAMPLE) (*cachep - 1); 922 } 923 } 924 } 925 926 927 METHODDEF(void) 928 pass2_fs_dither (j_decompress_ptr cinfo, 929 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) 930 /* This version performs Floyd-Steinberg dithering */ 931 { 932 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 933 hist3d histogram = cquantize->histogram; 934 register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */ 935 LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */ 936 LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */ 937 register FSERRPTR errorptr; /* => fserrors[] at column before current */ 938 JSAMPROW inptr; /* => current input pixel */ 939 JSAMPROW outptr; /* => current output pixel */ 940 histptr cachep; 941 int dir; /* +1 or -1 depending on direction */ 942 int dir3; /* 3*dir, for advancing inptr & errorptr */ 943 int row; 944 JDIMENSION col; 945 JDIMENSION width = cinfo->output_width; 946 JSAMPLE *range_limit = cinfo->sample_range_limit; 947 int *error_limit = cquantize->error_limiter; 948 JSAMPROW colormap0 = cinfo->colormap[0]; 949 JSAMPROW colormap1 = cinfo->colormap[1]; 950 JSAMPROW colormap2 = cinfo->colormap[2]; 951 SHIFT_TEMPS 952 953 for (row = 0; row < num_rows; row++) { 954 inptr = input_buf[row]; 955 outptr = output_buf[row]; 956 if (cquantize->on_odd_row) { 957 /* work right to left in this row */ 958 inptr += (width-1) * 3; /* so point to rightmost pixel */ 959 outptr += width-1; 960 dir = -1; 961 dir3 = -3; 962 errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */ 963 cquantize->on_odd_row = FALSE; /* flip for next time */ 964 } else { 965 /* work left to right in this row */ 966 dir = 1; 967 dir3 = 3; 968 errorptr = cquantize->fserrors; /* => entry before first real column */ 969 cquantize->on_odd_row = TRUE; /* flip for next time */ 970 } 971 /* Preset error values: no error propagated to first pixel from left */ 972 cur0 = cur1 = cur2 = 0; 973 /* and no error propagated to row below yet */ 974 belowerr0 = belowerr1 = belowerr2 = 0; 975 bpreverr0 = bpreverr1 = bpreverr2 = 0; 976 977 for (col = width; col > 0; col--) { 978 /* curN holds the error propagated from the previous pixel on the 979 * current line. Add the error propagated from the previous line 980 * to form the complete error correction term for this pixel, and 981 * round the error term (which is expressed * 16) to an integer. 982 * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct 983 * for either sign of the error value. 984 * Note: errorptr points to *previous* column's array entry. 985 */ 986 cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4); 987 cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4); 988 cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4); 989 /* Limit the error using transfer function set by init_error_limit. 990 * See comments with init_error_limit for rationale. 991 */ 992 cur0 = error_limit[cur0]; 993 cur1 = error_limit[cur1]; 994 cur2 = error_limit[cur2]; 995 /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE. 996 * The maximum error is +- MAXJSAMPLE (or less with error limiting); 997 * this sets the required size of the range_limit array. 998 */ 999 cur0 += GETJSAMPLE(inptr[0]); 1000 cur1 += GETJSAMPLE(inptr[1]); 1001 cur2 += GETJSAMPLE(inptr[2]); 1002 cur0 = GETJSAMPLE(range_limit[cur0]); 1003 cur1 = GETJSAMPLE(range_limit[cur1]); 1004 cur2 = GETJSAMPLE(range_limit[cur2]); 1005 /* Index into the cache with adjusted pixel value */ 1006 cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT]; 1007 /* If we have not seen this color before, find nearest colormap */ 1008 /* entry and update the cache */ 1009 if (*cachep == 0) 1010 fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT); 1011 /* Now emit the colormap index for this cell */ 1012 { register int pixcode = *cachep - 1; 1013 *outptr = (JSAMPLE) pixcode; 1014 /* Compute representation error for this pixel */ 1015 cur0 -= GETJSAMPLE(colormap0[pixcode]); 1016 cur1 -= GETJSAMPLE(colormap1[pixcode]); 1017 cur2 -= GETJSAMPLE(colormap2[pixcode]); 1018 } 1019 /* Compute error fractions to be propagated to adjacent pixels. 1020 * Add these into the running sums, and simultaneously shift the 1021 * next-line error sums left by 1 column. 1022 */ 1023 { register LOCFSERROR bnexterr; 1024 1025 bnexterr = cur0; /* Process component 0 */ 1026 errorptr[0] = (FSERROR) (bpreverr0 + cur0 * 3); 1027 bpreverr0 = belowerr0 + cur0 * 5; 1028 belowerr0 = bnexterr; 1029 cur0 *= 7; 1030 bnexterr = cur1; /* Process component 1 */ 1031 errorptr[1] = (FSERROR) (bpreverr1 + cur1 * 3); 1032 bpreverr1 = belowerr1 + cur1 * 5; 1033 belowerr1 = bnexterr; 1034 cur1 *= 7; 1035 bnexterr = cur2; /* Process component 2 */ 1036 errorptr[2] = (FSERROR) (bpreverr2 + cur2 * 3); 1037 bpreverr2 = belowerr2 + cur2 * 5; 1038 belowerr2 = bnexterr; 1039 cur2 *= 7; 1040 } 1041 /* At this point curN contains the 7/16 error value to be propagated 1042 * to the next pixel on the current line, and all the errors for the 1043 * next line have been shifted over. We are therefore ready to move on. 1044 */ 1045 inptr += dir3; /* Advance pixel pointers to next column */ 1046 outptr += dir; 1047 errorptr += dir3; /* advance errorptr to current column */ 1048 } 1049 /* Post-loop cleanup: we must unload the final error values into the 1050 * final fserrors[] entry. Note we need not unload belowerrN because 1051 * it is for the dummy column before or after the actual array. 1052 */ 1053 errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */ 1054 errorptr[1] = (FSERROR) bpreverr1; 1055 errorptr[2] = (FSERROR) bpreverr2; 1056 } 1057 } 1058 1059 1060 /* 1061 * Initialize the error-limiting transfer function (lookup table). 1062 * The raw F-S error computation can potentially compute error values of up to 1063 * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be 1064 * much less, otherwise obviously wrong pixels will be created. (Typical 1065 * effects include weird fringes at color-area boundaries, isolated bright 1066 * pixels in a dark area, etc.) The standard advice for avoiding this problem 1067 * is to ensure that the "corners" of the color cube are allocated as output 1068 * colors; then repeated errors in the same direction cannot cause cascading 1069 * error buildup. However, that only prevents the error from getting 1070 * completely out of hand; Aaron Giles reports that error limiting improves 1071 * the results even with corner colors allocated. 1072 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty 1073 * well, but the smoother transfer function used below is even better. Thanks 1074 * to Aaron Giles for this idea. 1075 */ 1076 1077 LOCAL(void) 1078 init_error_limit (j_decompress_ptr cinfo) 1079 /* Allocate and fill in the error_limiter table */ 1080 { 1081 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1082 int * table; 1083 int in, out; 1084 1085 table = (int *) (*cinfo->mem->alloc_small) 1086 ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * sizeof(int)); 1087 table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */ 1088 cquantize->error_limiter = table; 1089 1090 #define STEPSIZE ((MAXJSAMPLE+1)/16) 1091 /* Map errors 1:1 up to +- MAXJSAMPLE/16 */ 1092 out = 0; 1093 for (in = 0; in < STEPSIZE; in++, out++) { 1094 table[in] = out; table[-in] = -out; 1095 } 1096 /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */ 1097 for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) { 1098 table[in] = out; table[-in] = -out; 1099 } 1100 /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */ 1101 for (; in <= MAXJSAMPLE; in++) { 1102 table[in] = out; table[-in] = -out; 1103 } 1104 #undef STEPSIZE 1105 } 1106 1107 1108 /* 1109 * Finish up at the end of each pass. 1110 */ 1111 1112 METHODDEF(void) 1113 finish_pass1 (j_decompress_ptr cinfo) 1114 { 1115 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1116 1117 /* Select the representative colors and fill in cinfo->colormap */ 1118 cinfo->colormap = cquantize->sv_colormap; 1119 select_colors(cinfo, cquantize->desired); 1120 /* Force next pass to zero the color index table */ 1121 cquantize->needs_zeroed = TRUE; 1122 } 1123 1124 1125 METHODDEF(void) 1126 finish_pass2 (j_decompress_ptr cinfo) 1127 { 1128 /* no work */ 1129 } 1130 1131 1132 /* 1133 * Initialize for each processing pass. 1134 */ 1135 1136 METHODDEF(void) 1137 start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan) 1138 { 1139 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1140 hist3d histogram = cquantize->histogram; 1141 int i; 1142 1143 /* Only F-S dithering or no dithering is supported. */ 1144 /* If user asks for ordered dither, give him F-S. */ 1145 if (cinfo->dither_mode != JDITHER_NONE) 1146 cinfo->dither_mode = JDITHER_FS; 1147 1148 if (is_pre_scan) { 1149 /* Set up method pointers */ 1150 cquantize->pub.color_quantize = prescan_quantize; 1151 cquantize->pub.finish_pass = finish_pass1; 1152 cquantize->needs_zeroed = TRUE; /* Always zero histogram */ 1153 } else { 1154 /* Set up method pointers */ 1155 if (cinfo->dither_mode == JDITHER_FS) 1156 cquantize->pub.color_quantize = pass2_fs_dither; 1157 else 1158 cquantize->pub.color_quantize = pass2_no_dither; 1159 cquantize->pub.finish_pass = finish_pass2; 1160 1161 /* Make sure color count is acceptable */ 1162 i = cinfo->actual_number_of_colors; 1163 if (i < 1) 1164 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1); 1165 if (i > MAXNUMCOLORS) 1166 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); 1167 1168 if (cinfo->dither_mode == JDITHER_FS) { 1169 size_t arraysize = (size_t) ((cinfo->output_width + 2) * 1170 (3 * sizeof(FSERROR))); 1171 /* Allocate Floyd-Steinberg workspace if we didn't already. */ 1172 if (cquantize->fserrors == NULL) 1173 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) 1174 ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize); 1175 /* Initialize the propagated errors to zero. */ 1176 jzero_far((void *) cquantize->fserrors, arraysize); 1177 /* Make the error-limit table if we didn't already. */ 1178 if (cquantize->error_limiter == NULL) 1179 init_error_limit(cinfo); 1180 cquantize->on_odd_row = FALSE; 1181 } 1182 1183 } 1184 /* Zero the histogram or inverse color map, if necessary */ 1185 if (cquantize->needs_zeroed) { 1186 for (i = 0; i < HIST_C0_ELEMS; i++) { 1187 jzero_far((void *) histogram[i], 1188 HIST_C1_ELEMS*HIST_C2_ELEMS * sizeof(histcell)); 1189 } 1190 cquantize->needs_zeroed = FALSE; 1191 } 1192 } 1193 1194 1195 /* 1196 * Switch to a new external colormap between output passes. 1197 */ 1198 1199 METHODDEF(void) 1200 new_color_map_2_quant (j_decompress_ptr cinfo) 1201 { 1202 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1203 1204 /* Reset the inverse color map */ 1205 cquantize->needs_zeroed = TRUE; 1206 } 1207 1208 1209 /* 1210 * Module initialization routine for 2-pass color quantization. 1211 */ 1212 1213 GLOBAL(void) 1214 jinit_2pass_quantizer (j_decompress_ptr cinfo) 1215 { 1216 my_cquantize_ptr cquantize; 1217 int i; 1218 1219 cquantize = (my_cquantize_ptr) 1220 (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, 1221 sizeof(my_cquantizer)); 1222 cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; 1223 cquantize->pub.start_pass = start_pass_2_quant; 1224 cquantize->pub.new_color_map = new_color_map_2_quant; 1225 cquantize->fserrors = NULL; /* flag optional arrays not allocated */ 1226 cquantize->error_limiter = NULL; 1227 1228 /* Make sure jdmaster didn't give me a case I can't handle */ 1229 if (cinfo->out_color_components != 3) 1230 ERREXIT(cinfo, JERR_NOTIMPL); 1231 1232 /* Allocate the histogram/inverse colormap storage */ 1233 cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small) 1234 ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * sizeof(hist2d)); 1235 for (i = 0; i < HIST_C0_ELEMS; i++) { 1236 cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large) 1237 ((j_common_ptr) cinfo, JPOOL_IMAGE, 1238 HIST_C1_ELEMS*HIST_C2_ELEMS * sizeof(histcell)); 1239 } 1240 cquantize->needs_zeroed = TRUE; /* histogram is garbage now */ 1241 1242 /* Allocate storage for the completed colormap, if required. 1243 * We do this now since it may affect the memory manager's space 1244 * calculations. 1245 */ 1246 if (cinfo->enable_2pass_quant) { 1247 /* Make sure color count is acceptable */ 1248 int desired = cinfo->desired_number_of_colors; 1249 /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */ 1250 if (desired < 8) 1251 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8); 1252 /* Make sure colormap indexes can be represented by JSAMPLEs */ 1253 if (desired > MAXNUMCOLORS) 1254 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); 1255 cquantize->sv_colormap = (*cinfo->mem->alloc_sarray) 1256 ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3); 1257 cquantize->desired = desired; 1258 } else 1259 cquantize->sv_colormap = NULL; 1260 1261 /* Only F-S dithering or no dithering is supported. */ 1262 /* If user asks for ordered dither, give him F-S. */ 1263 if (cinfo->dither_mode != JDITHER_NONE) 1264 cinfo->dither_mode = JDITHER_FS; 1265 1266 /* Allocate Floyd-Steinberg workspace if necessary. 1267 * This isn't really needed until pass 2, but again it may affect the memory 1268 * manager's space calculations. Although we will cope with a later change 1269 * in dither_mode, we do not promise to honor max_memory_to_use if 1270 * dither_mode changes. 1271 */ 1272 if (cinfo->dither_mode == JDITHER_FS) { 1273 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) 1274 ((j_common_ptr) cinfo, JPOOL_IMAGE, 1275 (size_t) ((cinfo->output_width + 2) * (3 * sizeof(FSERROR)))); 1276 /* Might as well create the error-limiting table too. */ 1277 init_error_limit(cinfo); 1278 } 1279 } 1280 1281 #endif /* QUANT_2PASS_SUPPORTED */ 1282