440 lines
13 KiB
C
440 lines
13 KiB
C
// Copyright 2011 Google Inc. All Rights Reserved.
|
|
//
|
|
// This code is licensed under the same terms as WebM:
|
|
// Software License Agreement: http://www.webmproject.org/license/software/
|
|
// Additional IP Rights Grant: http://www.webmproject.org/license/additional/
|
|
// -----------------------------------------------------------------------------
|
|
//
|
|
// Author: Jyrki Alakuijala (jyrki@google.com)
|
|
//
|
|
// Entropy encoding (Huffman) for webp lossless.
|
|
|
|
#include <assert.h>
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
#include "./huffman_encode.h"
|
|
#include "../utils/utils.h"
|
|
#include "../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
|
|
// Hufmann tree compression, especially its RLE-part, give smaller output.
|
|
static int OptimizeHuffmanForRle(int length, int* const counts) {
|
|
uint8_t* good_for_rle;
|
|
// 1) Let's make the Huffman code more compatible with rle encoding.
|
|
int i;
|
|
for (; length >= 0; --length) {
|
|
if (length == 0) {
|
|
return 1; // 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.
|
|
good_for_rle = (uint8_t*)calloc(length, 1);
|
|
if (good_for_rle == NULL) {
|
|
return 0;
|
|
}
|
|
{
|
|
// 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.
|
|
int 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.
|
|
{
|
|
int stride = 0;
|
|
int limit = counts[0];
|
|
int 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)) {
|
|
int k;
|
|
// The stride must end, collapse what we have, if we have enough (4).
|
|
int 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;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
free(good_for_rle);
|
|
return 1;
|
|
}
|
|
|
|
typedef struct {
|
|
int total_count_;
|
|
int value_;
|
|
int pool_index_left_;
|
|
int pool_index_right_;
|
|
} HuffmanTree;
|
|
|
|
// 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 {
|
|
if (t1->value_ < t2->value_) {
|
|
return -1;
|
|
}
|
|
if (t1->value_ > t2->value_) {
|
|
return 1;
|
|
}
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
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 http://en.wikipedia.org/wiki/Huffman_coding
|
|
static int GenerateOptimalTree(const int* const histogram, int histogram_size,
|
|
int tree_depth_limit,
|
|
uint8_t* const bit_depths) {
|
|
int count_min;
|
|
HuffmanTree* tree_pool;
|
|
HuffmanTree* tree;
|
|
int tree_size_orig = 0;
|
|
int i;
|
|
|
|
for (i = 0; i < histogram_size; ++i) {
|
|
if (histogram[i] != 0) {
|
|
++tree_size_orig;
|
|
}
|
|
}
|
|
|
|
// 3 * tree_size is enough to cover all the nodes representing a
|
|
// population and all the inserted nodes combining two existing nodes.
|
|
// The tree pool needs 2 * (tree_size_orig - 1) entities, and the
|
|
// tree needs exactly tree_size_orig entities.
|
|
tree = (HuffmanTree*)WebPSafeMalloc(3ULL * tree_size_orig, sizeof(*tree));
|
|
if (tree == NULL) return 0;
|
|
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 int 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.
|
|
int 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;
|
|
}
|
|
}
|
|
}
|
|
free(tree);
|
|
return 1;
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------
|
|
// 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
|
|
|
|
int VP8LCreateHuffmanTree(int* const histogram, int tree_depth_limit,
|
|
HuffmanTreeCode* const tree) {
|
|
const int num_symbols = tree->num_symbols;
|
|
if (!OptimizeHuffmanForRle(num_symbols, histogram)) {
|
|
return 0;
|
|
}
|
|
if (!GenerateOptimalTree(histogram, num_symbols,
|
|
tree_depth_limit, tree->code_lengths)) {
|
|
return 0;
|
|
}
|
|
// Create the actual bit codes for the bit lengths.
|
|
ConvertBitDepthsToSymbols(tree);
|
|
return 1;
|
|
}
|