tor-browser

palette.c (14146B)

---

// Copyright 2023 Google Inc. All Rights Reserved.
//
// Use of this source code is governed by a BSD-style license
// that can be found in the COPYING file in the root of the source
// tree. An additional intellectual property rights grant can be found
// in the file PATENTS. All contributing project authors may
// be found in the AUTHORS file in the root of the source tree.
// -----------------------------------------------------------------------------
//
// Utilities for palette analysis.
//
// Author: Vincent Rabaud (vrabaud@google.com)

#include "src/utils/palette.h"

#include <assert.h>
#include <stdlib.h>
#include <string.h>

#include "src/dsp/lossless_common.h"
#include "src/utils/color_cache_utils.h"
#include "src/utils/utils.h"
#include "src/webp/encode.h"
#include "src/webp/format_constants.h"
#include "src/webp/types.h"

// -----------------------------------------------------------------------------

// Palette reordering for smaller sum of deltas (and for smaller storage).

static int PaletteCompareColorsForQsort(const void* p1, const void* p2) {
 const uint32_t a = WebPMemToUint32((uint8_t*)p1);
 const uint32_t b = WebPMemToUint32((uint8_t*)p2);
 assert(a != b);
 return (a < b) ? -1 : 1;
}

static WEBP_INLINE uint32_t PaletteComponentDistance(uint32_t v) {
 return (v <= 128) ? v : (256 - v);
}

// Computes a value that is related to the entropy created by the
// palette entry diff.
//
// Note that the last & 0xff is a no-operation in the next statement, but
// removed by most compilers and is here only for regularity of the code.
static WEBP_INLINE uint32_t PaletteColorDistance(uint32_t col1, uint32_t col2) {
 const uint32_t diff = VP8LSubPixels(col1, col2);
 const int kMoreWeightForRGBThanForAlpha = 9;
 uint32_t score;
 score = PaletteComponentDistance((diff >> 0) & 0xff);
 score += PaletteComponentDistance((diff >> 8) & 0xff);
 score += PaletteComponentDistance((diff >> 16) & 0xff);
 score *= kMoreWeightForRGBThanForAlpha;
 score += PaletteComponentDistance((diff >> 24) & 0xff);
 return score;
}

static WEBP_INLINE void SwapColor(uint32_t* const col1, uint32_t* const col2) {
 const uint32_t tmp = *col1;
 *col1 = *col2;
 *col2 = tmp;
}

int SearchColorNoIdx(const uint32_t sorted[], uint32_t color, int num_colors) {
 int low = 0, hi = num_colors;
 if (sorted[low] == color) return low;  // loop invariant: sorted[low] != color
 while (1) {
   const int mid = (low + hi) >> 1;
   if (sorted[mid] == color) {
     return mid;
   } else if (sorted[mid] < color) {
     low = mid;
   } else {
     hi = mid;
   }
 }
 assert(0);
 return 0;
}

void PrepareMapToPalette(const uint32_t palette[], uint32_t num_colors,
                        uint32_t sorted[], uint32_t idx_map[]) {
 uint32_t i;
 memcpy(sorted, palette, num_colors * sizeof(*sorted));
 qsort(sorted, num_colors, sizeof(*sorted), PaletteCompareColorsForQsort);
 for (i = 0; i < num_colors; ++i) {
   idx_map[SearchColorNoIdx(sorted, palette[i], num_colors)] = i;
 }
}

//------------------------------------------------------------------------------

#define COLOR_HASH_SIZE (MAX_PALETTE_SIZE * 4)
#define COLOR_HASH_RIGHT_SHIFT 22  // 32 - log2(COLOR_HASH_SIZE).

int GetColorPalette(const WebPPicture* const pic, uint32_t* const palette) {
 int i;
 int x, y;
 int num_colors = 0;
 uint8_t in_use[COLOR_HASH_SIZE] = {0};
 uint32_t colors[COLOR_HASH_SIZE] = {0};
 const uint32_t* argb = pic->argb;
 const int width = pic->width;
 const int height = pic->height;
 uint32_t last_pix = ~argb[0];  // so we're sure that last_pix != argb[0]
 assert(pic != NULL);
 assert(pic->use_argb);

 for (y = 0; y < height; ++y) {
   for (x = 0; x < width; ++x) {
     int key;
     if (argb[x] == last_pix) {
       continue;
     }
     last_pix = argb[x];
     key = VP8LHashPix(last_pix, COLOR_HASH_RIGHT_SHIFT);
     while (1) {
       if (!in_use[key]) {
         colors[key] = last_pix;
         in_use[key] = 1;
         ++num_colors;
         if (num_colors > MAX_PALETTE_SIZE) {
           return MAX_PALETTE_SIZE + 1;  // Exact count not needed.
         }
         break;
       } else if (colors[key] == last_pix) {
         break;  // The color is already there.
       } else {
         // Some other color sits here, so do linear conflict resolution.
         ++key;
         key &= (COLOR_HASH_SIZE - 1);  // Key mask.
       }
     }
   }
   argb += pic->argb_stride;
 }

 if (palette != NULL) {  // Fill the colors into palette.
   num_colors = 0;
   for (i = 0; i < COLOR_HASH_SIZE; ++i) {
     if (in_use[i]) {
       palette[num_colors] = colors[i];
       ++num_colors;
     }
   }
   qsort(palette, num_colors, sizeof(*palette), PaletteCompareColorsForQsort);
 }
 return num_colors;
}

#undef COLOR_HASH_SIZE
#undef COLOR_HASH_RIGHT_SHIFT

// -----------------------------------------------------------------------------

// The palette has been sorted by alpha. This function checks if the other
// components of the palette have a monotonic development with regards to
// position in the palette. If all have monotonic development, there is
// no benefit to re-organize them greedily. A monotonic development
// would be spotted in green-only situations (like lossy alpha) or gray-scale
// images.
static int PaletteHasNonMonotonousDeltas(const uint32_t* const palette,
                                        int num_colors) {
 uint32_t predict = 0x000000;
 int i;
 uint8_t sign_found = 0x00;
 for (i = 0; i < num_colors; ++i) {
   const uint32_t diff = VP8LSubPixels(palette[i], predict);
   const uint8_t rd = (diff >> 16) & 0xff;
   const uint8_t gd = (diff >> 8) & 0xff;
   const uint8_t bd = (diff >> 0) & 0xff;
   if (rd != 0x00) {
     sign_found |= (rd < 0x80) ? 1 : 2;
   }
   if (gd != 0x00) {
     sign_found |= (gd < 0x80) ? 8 : 16;
   }
   if (bd != 0x00) {
     sign_found |= (bd < 0x80) ? 64 : 128;
   }
   predict = palette[i];
 }
 return (sign_found & (sign_found << 1)) != 0;  // two consequent signs.
}

static void PaletteSortMinimizeDeltas(const uint32_t* const palette_sorted,
                                     int num_colors, uint32_t* const palette) {
 uint32_t predict = 0x00000000;
 int i, k;
 memcpy(palette, palette_sorted, num_colors * sizeof(*palette));
 if (!PaletteHasNonMonotonousDeltas(palette_sorted, num_colors)) return;
 // Find greedily always the closest color of the predicted color to minimize
 // deltas in the palette. This reduces storage needs since the
 // palette is stored with delta encoding.
 if (num_colors > 17) {
   if (palette[0] == 0) {
     --num_colors;
     SwapColor(&palette[num_colors], &palette[0]);
   }
 }
 for (i = 0; i < num_colors; ++i) {
   int best_ix = i;
   uint32_t best_score = ~0U;
   for (k = i; k < num_colors; ++k) {
     const uint32_t cur_score = PaletteColorDistance(palette[k], predict);
     if (best_score > cur_score) {
       best_score = cur_score;
       best_ix = k;
     }
   }
   SwapColor(&palette[best_ix], &palette[i]);
   predict = palette[i];
 }
}

// -----------------------------------------------------------------------------
// Modified Zeng method from "A Survey on Palette Reordering
// Methods for Improving the Compression of Color-Indexed Images" by Armando J.
// Pinho and Antonio J. R. Neves.

// Finds the biggest cooccurrence in the matrix.
static void CoOccurrenceFindMax(const uint32_t* const cooccurrence,
                               uint32_t num_colors, uint8_t* const c1,
                               uint8_t* const c2) {
 // Find the index that is most frequently located adjacent to other
 // (different) indexes.
 uint32_t best_sum = 0u;
 uint32_t i, j, best_cooccurrence;
 *c1 = 0u;
 for (i = 0; i < num_colors; ++i) {
   uint32_t sum = 0;
   for (j = 0; j < num_colors; ++j) sum += cooccurrence[i * num_colors + j];
   if (sum > best_sum) {
     best_sum = sum;
     *c1 = i;
   }
 }
 // Find the index that is most frequently found adjacent to *c1.
 *c2 = 0u;
 best_cooccurrence = 0u;
 for (i = 0; i < num_colors; ++i) {
   if (cooccurrence[*c1 * num_colors + i] > best_cooccurrence) {
     best_cooccurrence = cooccurrence[*c1 * num_colors + i];
     *c2 = i;
   }
 }
 assert(*c1 != *c2);
}

// Builds the cooccurrence matrix
static int CoOccurrenceBuild(const WebPPicture* const pic,
                            const uint32_t* const palette, uint32_t num_colors,
                            uint32_t* cooccurrence) {
 uint32_t *lines, *line_top, *line_current, *line_tmp;
 int x, y;
 const uint32_t* src = pic->argb;
 uint32_t prev_pix = ~src[0];
 uint32_t prev_idx = 0u;
 uint32_t idx_map[MAX_PALETTE_SIZE] = {0};
 uint32_t palette_sorted[MAX_PALETTE_SIZE];
 lines = (uint32_t*)WebPSafeMalloc(2 * pic->width, sizeof(*lines));
 if (lines == NULL) {
   return 0;
 }
 line_top = &lines[0];
 line_current = &lines[pic->width];
 PrepareMapToPalette(palette, num_colors, palette_sorted, idx_map);
 for (y = 0; y < pic->height; ++y) {
   for (x = 0; x < pic->width; ++x) {
     const uint32_t pix = src[x];
     if (pix != prev_pix) {
       prev_idx = idx_map[SearchColorNoIdx(palette_sorted, pix, num_colors)];
       prev_pix = pix;
     }
     line_current[x] = prev_idx;
     // 4-connectivity is what works best as mentioned in "On the relation
     // between Memon's and the modified Zeng's palette reordering methods".
     if (x > 0 && prev_idx != line_current[x - 1]) {
       const uint32_t left_idx = line_current[x - 1];
       ++cooccurrence[prev_idx * num_colors + left_idx];
       ++cooccurrence[left_idx * num_colors + prev_idx];
     }
     if (y > 0 && prev_idx != line_top[x]) {
       const uint32_t top_idx = line_top[x];
       ++cooccurrence[prev_idx * num_colors + top_idx];
       ++cooccurrence[top_idx * num_colors + prev_idx];
     }
   }
   line_tmp = line_top;
   line_top = line_current;
   line_current = line_tmp;
   src += pic->argb_stride;
 }
 WebPSafeFree(lines);
 return 1;
}

struct Sum {
 uint8_t index;
 uint32_t sum;
};

static int PaletteSortModifiedZeng(const WebPPicture* const pic,
                                  const uint32_t* const palette_in,
                                  uint32_t num_colors,
                                  uint32_t* const palette) {
 uint32_t i, j, ind;
 uint8_t remapping[MAX_PALETTE_SIZE];
 uint32_t* cooccurrence;
 struct Sum sums[MAX_PALETTE_SIZE];
 uint32_t first, last;
 uint32_t num_sums;
 // TODO(vrabaud) check whether one color images should use palette or not.
 if (num_colors <= 1) return 1;
 // Build the co-occurrence matrix.
 cooccurrence =
     (uint32_t*)WebPSafeCalloc(num_colors * num_colors, sizeof(*cooccurrence));
 if (cooccurrence == NULL) {
   return 0;
 }
 if (!CoOccurrenceBuild(pic, palette_in, num_colors, cooccurrence)) {
   WebPSafeFree(cooccurrence);
   return 0;
 }

 // Initialize the mapping list with the two best indices.
 CoOccurrenceFindMax(cooccurrence, num_colors, &remapping[0], &remapping[1]);

 // We need to append and prepend to the list of remapping. To this end, we
 // actually define the next start/end of the list as indices in a vector (with
 // a wrap around when the end is reached).
 first = 0;
 last = 1;
 num_sums = num_colors - 2;  // -2 because we know the first two values
 if (num_sums > 0) {
   // Initialize the sums with the first two remappings and find the best one
   struct Sum* best_sum = &sums[0];
   best_sum->index = 0u;
   best_sum->sum = 0u;
   for (i = 0, j = 0; i < num_colors; ++i) {
     if (i == remapping[0] || i == remapping[1]) continue;
     sums[j].index = i;
     sums[j].sum = cooccurrence[i * num_colors + remapping[0]] +
                   cooccurrence[i * num_colors + remapping[1]];
     if (sums[j].sum > best_sum->sum) best_sum = &sums[j];
     ++j;
   }

   while (num_sums > 0) {
     const uint8_t best_index = best_sum->index;
     // Compute delta to know if we need to prepend or append the best index.
     int32_t delta = 0;
     const int32_t n = num_colors - num_sums;
     for (ind = first, j = 0; (ind + j) % num_colors != last + 1; ++j) {
       const uint16_t l_j = remapping[(ind + j) % num_colors];
       delta += (n - 1 - 2 * (int32_t)j) *
                (int32_t)cooccurrence[best_index * num_colors + l_j];
     }
     if (delta > 0) {
       first = (first == 0) ? num_colors - 1 : first - 1;
       remapping[first] = best_index;
     } else {
       ++last;
       remapping[last] = best_index;
     }
     // Remove best_sum from sums.
     *best_sum = sums[num_sums - 1];
     --num_sums;
     // Update all the sums and find the best one.
     best_sum = &sums[0];
     for (i = 0; i < num_sums; ++i) {
       sums[i].sum += cooccurrence[best_index * num_colors + sums[i].index];
       if (sums[i].sum > best_sum->sum) best_sum = &sums[i];
     }
   }
 }
 assert((last + 1) % num_colors == first);
 WebPSafeFree(cooccurrence);

 // Re-map the palette.
 for (i = 0; i < num_colors; ++i) {
   palette[i] = palette_in[remapping[(first + i) % num_colors]];
 }
 return 1;
}

// -----------------------------------------------------------------------------

int PaletteSort(PaletteSorting method, const struct WebPPicture* const pic,
               const uint32_t* const palette_sorted, uint32_t num_colors,
               uint32_t* const palette) {
 switch (method) {
   case kSortedDefault:
     if (palette_sorted[0] == 0 && num_colors > 17) {
       memcpy(palette, palette_sorted + 1,
              (num_colors - 1) * sizeof(*palette_sorted));
       palette[num_colors - 1] = 0;
     } else {
       memcpy(palette, palette_sorted, num_colors * sizeof(*palette));
     }
     return 1;
   case kMinimizeDelta:
     PaletteSortMinimizeDeltas(palette_sorted, num_colors, palette);
     return 1;
   case kModifiedZeng:
     return PaletteSortModifiedZeng(pic, palette_sorted, num_colors, palette);
   case kUnusedPalette:
   case kPaletteSortingNum:
     break;
 }

 assert(0);
 return 0;
}