go-libwebp

huffman_encode_utils.c (13526B)

---

 // Copyright 2011 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.
 // -----------------------------------------------------------------------------
 //
 // Author: Jyrki Alakuijala (jyrki@google.com)
 //
 // Entropy encoding (Huffman) for webp lossless.
 
 #include <assert.h>
 #include <stdlib.h>
 #include <string.h>
 
 #include "src/utils/huffman_encode_utils.h"
 #include "src/webp/types.h"
 #include "src/utils/utils.h"
 #include "src/webp/format_constants.h"
 
 // -----------------------------------------------------------------------------
 // Util function to optimize the symbol map for RLE coding
 
 // Heuristics for selecting the stride ranges to collapse.
 static int ValuesShouldBeCollapsedToStrideAverage(int a, int b) {
   return abs(a - b) < 4;
 }
 
 // Change the population counts in a way that the consequent
 // Huffman tree compression, especially its RLE-part, give smaller output.
 static void OptimizeHuffmanForRle(int length, uint8_t* const good_for_rle,
                                   uint32_t* const counts) {
   // 1) Let's make the Huffman code more compatible with rle encoding.
   int i;
   for (; length >= 0; --length) {
     if (length == 0) {
       return;  // All zeros.
     }
     if (counts[length - 1] != 0) {
       // Now counts[0..length - 1] does not have trailing zeros.
       break;
     }
   }
   // 2) Let's mark all population counts that already can be encoded
   // with an rle code.
   {
     // Let's not spoil any of the existing good rle codes.
     // Mark any seq of 0's that is longer as 5 as a good_for_rle.
     // Mark any seq of non-0's that is longer as 7 as a good_for_rle.
     uint32_t symbol = counts[0];
     int stride = 0;
     for (i = 0; i < length + 1; ++i) {
       if (i == length || counts[i] != symbol) {
         if ((symbol == 0 && stride >= 5) ||
             (symbol != 0 && stride >= 7)) {
           int k;
           for (k = 0; k < stride; ++k) {
             good_for_rle[i - k - 1] = 1;
           }
         }
         stride = 1;
         if (i != length) {
           symbol = counts[i];
         }
       } else {
         ++stride;
       }
     }
   }
   // 3) Let's replace those population counts that lead to more rle codes.
   {
     uint32_t stride = 0;
     uint32_t limit = counts[0];
     uint32_t sum = 0;
     for (i = 0; i < length + 1; ++i) {
       if (i == length || good_for_rle[i] ||
           (i != 0 && good_for_rle[i - 1]) ||
           !ValuesShouldBeCollapsedToStrideAverage(counts[i], limit)) {
         if (stride >= 4 || (stride >= 3 && sum == 0)) {
           uint32_t k;
           // The stride must end, collapse what we have, if we have enough (4).
           uint32_t count = (sum + stride / 2) / stride;
           if (count < 1) {
             count = 1;
           }
           if (sum == 0) {
             // Don't make an all zeros stride to be upgraded to ones.
             count = 0;
           }
           for (k = 0; k < stride; ++k) {
             // We don't want to change value at counts[i],
             // that is already belonging to the next stride. Thus - 1.
             counts[i - k - 1] = count;
           }
         }
         stride = 0;
         sum = 0;
         if (i < length - 3) {
           // All interesting strides have a count of at least 4,
           // at least when non-zeros.
           limit = (counts[i] + counts[i + 1] +
                    counts[i + 2] + counts[i + 3] + 2) / 4;
         } else if (i < length) {
           limit = counts[i];
         } else {
           limit = 0;
         }
       }
       ++stride;
       if (i != length) {
         sum += counts[i];
         if (stride >= 4) {
           limit = (sum + stride / 2) / stride;
         }
       }
     }
   }
 }
 
 // A comparer function for two Huffman trees: sorts first by 'total count'
 // (more comes first), and then by 'value' (more comes first).
 static int CompareHuffmanTrees(const void* ptr1, const void* ptr2) {
   const HuffmanTree* const t1 = (const HuffmanTree*)ptr1;
   const HuffmanTree* const t2 = (const HuffmanTree*)ptr2;
   if (t1->total_count > t2->total_count) {
     return -1;
   } else if (t1->total_count < t2->total_count) {
     return 1;
   } else {
     assert(t1->value != t2->value);
     return (t1->value < t2->value) ? -1 : 1;
   }
 }
 
 static void SetBitDepths(const HuffmanTree* const tree,
                          const HuffmanTree* const pool,
                          uint8_t* const bit_depths, int level) {
   if (tree->pool_index_left >= 0) {
     SetBitDepths(&pool[tree->pool_index_left], pool, bit_depths, level + 1);
     SetBitDepths(&pool[tree->pool_index_right], pool, bit_depths, level + 1);
   } else {
     bit_depths[tree->value] = level;
   }
 }
 
 // Create an optimal Huffman tree.
 //
 // (data,length): population counts.
 // tree_limit: maximum bit depth (inclusive) of the codes.
 // bit_depths[]: how many bits are used for the symbol.
 //
 // Returns 0 when an error has occurred.
 //
 // The catch here is that the tree cannot be arbitrarily deep
 //
 // count_limit is the value that is to be faked as the minimum value
 // and this minimum value is raised until the tree matches the
 // maximum length requirement.
 //
 // This algorithm is not of excellent performance for very long data blocks,
 // especially when population counts are longer than 2**tree_limit, but
 // we are not planning to use this with extremely long blocks.
 //
 // See https://en.wikipedia.org/wiki/Huffman_coding
 static void GenerateOptimalTree(const uint32_t* const histogram,
                                 int histogram_size,
                                 HuffmanTree* tree, int tree_depth_limit,
                                 uint8_t* const bit_depths) {
   uint32_t count_min;
   HuffmanTree* tree_pool;
   int tree_size_orig = 0;
   int i;
 
   for (i = 0; i < histogram_size; ++i) {
     if (histogram[i] != 0) {
       ++tree_size_orig;
     }
   }
 
   if (tree_size_orig == 0) {   // pretty optimal already!
     return;
   }
 
   tree_pool = tree + tree_size_orig;
 
   // For block sizes with less than 64k symbols we never need to do a
   // second iteration of this loop.
   // If we actually start running inside this loop a lot, we would perhaps
   // be better off with the Katajainen algorithm.
   assert(tree_size_orig <= (1 << (tree_depth_limit - 1)));
   for (count_min = 1; ; count_min *= 2) {
     int tree_size = tree_size_orig;
     // We need to pack the Huffman tree in tree_depth_limit bits.
     // So, we try by faking histogram entries to be at least 'count_min'.
     int idx = 0;
     int j;
     for (j = 0; j < histogram_size; ++j) {
       if (histogram[j] != 0) {
         const uint32_t count =
             (histogram[j] < count_min) ? count_min : histogram[j];
         tree[idx].total_count = count;
         tree[idx].value = j;
         tree[idx].pool_index_left = -1;
         tree[idx].pool_index_right = -1;
         ++idx;
       }
     }
 
     // Build the Huffman tree.
     qsort(tree, tree_size, sizeof(*tree), CompareHuffmanTrees);
 
     if (tree_size > 1) {  // Normal case.
       int tree_pool_size = 0;
       while (tree_size > 1) {  // Finish when we have only one root.
         uint32_t count;
         tree_pool[tree_pool_size++] = tree[tree_size - 1];
         tree_pool[tree_pool_size++] = tree[tree_size - 2];
         count = tree_pool[tree_pool_size - 1].total_count +
                 tree_pool[tree_pool_size - 2].total_count;
         tree_size -= 2;
         {
           // Search for the insertion point.
           int k;
           for (k = 0; k < tree_size; ++k) {
             if (tree[k].total_count <= count) {
               break;
             }
           }
           memmove(tree + (k + 1), tree + k, (tree_size - k) * sizeof(*tree));
           tree[k].total_count = count;
           tree[k].value = -1;
 
           tree[k].pool_index_left = tree_pool_size - 1;
           tree[k].pool_index_right = tree_pool_size - 2;
           tree_size = tree_size + 1;
         }
       }
       SetBitDepths(&tree[0], tree_pool, bit_depths, 0);
     } else if (tree_size == 1) {  // Trivial case: only one element.
       bit_depths[tree[0].value] = 1;
     }
 
     {
       // Test if this Huffman tree satisfies our 'tree_depth_limit' criteria.
       int max_depth = bit_depths[0];
       for (j = 1; j < histogram_size; ++j) {
         if (max_depth < bit_depths[j]) {
           max_depth = bit_depths[j];
         }
       }
       if (max_depth <= tree_depth_limit) {
         break;
       }
     }
   }
 }
 
 // -----------------------------------------------------------------------------
 // Coding of the Huffman tree values
 
 static HuffmanTreeToken* CodeRepeatedValues(int repetitions,
                                             HuffmanTreeToken* tokens,
                                             int value, int prev_value) {
   assert(value <= MAX_ALLOWED_CODE_LENGTH);
   if (value != prev_value) {
     tokens->code = value;
     tokens->extra_bits = 0;
     ++tokens;
     --repetitions;
   }
   while (repetitions >= 1) {
     if (repetitions < 3) {
       int i;
       for (i = 0; i < repetitions; ++i) {
         tokens->code = value;
         tokens->extra_bits = 0;
         ++tokens;
       }
       break;
     } else if (repetitions < 7) {
       tokens->code = 16;
       tokens->extra_bits = repetitions - 3;
       ++tokens;
       break;
     } else {
       tokens->code = 16;
       tokens->extra_bits = 3;
       ++tokens;
       repetitions -= 6;
     }
   }
   return tokens;
 }
 
 static HuffmanTreeToken* CodeRepeatedZeros(int repetitions,
                                            HuffmanTreeToken* tokens) {
   while (repetitions >= 1) {
     if (repetitions < 3) {
       int i;
       for (i = 0; i < repetitions; ++i) {
         tokens->code = 0;   // 0-value
         tokens->extra_bits = 0;
         ++tokens;
       }
       break;
     } else if (repetitions < 11) {
       tokens->code = 17;
       tokens->extra_bits = repetitions - 3;
       ++tokens;
       break;
     } else if (repetitions < 139) {
       tokens->code = 18;
       tokens->extra_bits = repetitions - 11;
       ++tokens;
       break;
     } else {
       tokens->code = 18;
       tokens->extra_bits = 0x7f;  // 138 repeated 0s
       ++tokens;
       repetitions -= 138;
     }
   }
   return tokens;
 }
 
 int VP8LCreateCompressedHuffmanTree(const HuffmanTreeCode* const tree,
                                     HuffmanTreeToken* tokens, int max_tokens) {
   HuffmanTreeToken* const starting_token = tokens;
   HuffmanTreeToken* const ending_token = tokens + max_tokens;
   const int depth_size = tree->num_symbols;
   int prev_value = 8;  // 8 is the initial value for rle.
   int i = 0;
   assert(tokens != NULL);
   while (i < depth_size) {
     const int value = tree->code_lengths[i];
     int k = i + 1;
     int runs;
     while (k < depth_size && tree->code_lengths[k] == value) ++k;
     runs = k - i;
     if (value == 0) {
       tokens = CodeRepeatedZeros(runs, tokens);
     } else {
       tokens = CodeRepeatedValues(runs, tokens, value, prev_value);
       prev_value = value;
     }
     i += runs;
     assert(tokens <= ending_token);
   }
   (void)ending_token;    // suppress 'unused variable' warning
   return (int)(tokens - starting_token);
 }
 
 // -----------------------------------------------------------------------------
 
 // Pre-reversed 4-bit values.
 static const uint8_t kReversedBits[16] = {
   0x0, 0x8, 0x4, 0xc, 0x2, 0xa, 0x6, 0xe,
   0x1, 0x9, 0x5, 0xd, 0x3, 0xb, 0x7, 0xf
 };
 
 static uint32_t ReverseBits(int num_bits, uint32_t bits) {
   uint32_t retval = 0;
   int i = 0;
   while (i < num_bits) {
     i += 4;
     retval |= kReversedBits[bits & 0xf] << (MAX_ALLOWED_CODE_LENGTH + 1 - i);
     bits >>= 4;
   }
   retval >>= (MAX_ALLOWED_CODE_LENGTH + 1 - num_bits);
   return retval;
 }
 
 // Get the actual bit values for a tree of bit depths.
 static void ConvertBitDepthsToSymbols(HuffmanTreeCode* const tree) {
   // 0 bit-depth means that the symbol does not exist.
   int i;
   int len;
   uint32_t next_code[MAX_ALLOWED_CODE_LENGTH + 1];
   int depth_count[MAX_ALLOWED_CODE_LENGTH + 1] = { 0 };
 
   assert(tree != NULL);
   len = tree->num_symbols;
   for (i = 0; i < len; ++i) {
     const int code_length = tree->code_lengths[i];
     assert(code_length <= MAX_ALLOWED_CODE_LENGTH);
     ++depth_count[code_length];
   }
   depth_count[0] = 0;  // ignore unused symbol
   next_code[0] = 0;
   {
     uint32_t code = 0;
     for (i = 1; i <= MAX_ALLOWED_CODE_LENGTH; ++i) {
       code = (code + depth_count[i - 1]) << 1;
       next_code[i] = code;
     }
   }
   for (i = 0; i < len; ++i) {
     const int code_length = tree->code_lengths[i];
     tree->codes[i] = ReverseBits(code_length, next_code[code_length]++);
   }
 }
 
 // -----------------------------------------------------------------------------
 // Main entry point
 
 void VP8LCreateHuffmanTree(uint32_t* const histogram, int tree_depth_limit,
                            uint8_t* const buf_rle, HuffmanTree* const huff_tree,
                            HuffmanTreeCode* const huff_code) {
   const int num_symbols = huff_code->num_symbols;
   memset(buf_rle, 0, num_symbols * sizeof(*buf_rle));
   OptimizeHuffmanForRle(num_symbols, buf_rle, histogram);
   GenerateOptimalTree(histogram, num_symbols, huff_tree, tree_depth_limit,
                       huff_code->code_lengths);
   // Create the actual bit codes for the bit lengths.
   ConvertBitDepthsToSymbols(huff_code);
 }