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