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// Copyright 2013 Google Inc. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// Functions to estimate the bit cost of Huffman trees.
#ifndef BROTLI_ENC_BIT_COST_H_
#define BROTLI_ENC_BIT_COST_H_
#include <stdint.h>
#include "./entropy_encode.h"
#include "./fast_log.h"
namespace brotli {
static const int kHuffmanExtraBits[kCodeLengthCodes] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 7,
};
static inline int HuffmanTreeBitCost(const int* counts, const uint8_t* depth) {
int nbits = 0;
for (int i = 0; i < kCodeLengthCodes; ++i) {
nbits += counts[i] * (depth[i] + kHuffmanExtraBits[i]);
}
return nbits;
}
static inline int HuffmanTreeBitCost(
const Histogram<kCodeLengthCodes>& histogram,
const EntropyCode<kCodeLengthCodes>& entropy) {
return HuffmanTreeBitCost(&histogram.data_[0], &entropy.depth_[0]);
}
static inline int HuffmanBitCost(const uint8_t* depth, int length) {
int max_depth = 1;
int histogram[kCodeLengthCodes] = { 0 };
int tail_start = 0;
// compute histogram of compacted huffman tree
for (int i = 0; i < length;) {
const int value = depth[i];
if (value > max_depth) {
max_depth = value;
}
int reps = 1;
for (int k = i + 1; k < length && depth[k] == value; ++k) {
++reps;
}
i += reps;
if (value == 0) {
while (reps > 10) {
++histogram[18];
reps -= 138;
}
if (reps > 2) {
++histogram[17];
} else if (reps > 0) {
histogram[0] += reps;
}
} else {
tail_start = i;
++histogram[value];
--reps;
while (reps > 2) {
++histogram[16];
reps -= 6;
}
if (reps > 0) {
histogram[value] += reps;
}
}
}
// create huffman tree of huffman tree
uint8_t cost[kCodeLengthCodes] = { 0 };
CreateHuffmanTree(histogram, kCodeLengthCodes, 7, cost);
// account for rle extra bits
cost[16] += 2;
cost[17] += 3;
cost[18] += 7;
int tree_size = 0;
int bits = 6 + 3 * max_depth; // huffman tree of huffman tree cost
for (int i = 0; i < kCodeLengthCodes; ++i) {
bits += histogram[i] * cost[i]; // huffman tree bit cost
tree_size += histogram[i];
}
// bit cost adjustment for long trailing zero sequence
int tail_size = length - tail_start;
int tail_bits = 0;
while (tail_size >= 1) {
if (tail_size < 3) {
tail_bits += tail_size * cost[0];
tree_size -= tail_size;
break;
} else if (tail_size < 11) {
tail_bits += cost[17];
--tree_size;
break;
} else {
tail_bits += cost[18];
tail_size -= 138;
--tree_size;
}
}
if (tail_bits > 12) {
bits += ((Log2Ceiling(tree_size - 1) + 1) & ~1) + 3 - tail_bits;
}
return bits;
}
template<int kSize>
double PopulationCost(const Histogram<kSize>& histogram) {
if (histogram.total_count_ == 0) {
return 4;
}
int symbols[2] = { 0 };
int count = 0;
for (int i = 0; i < kSize && count < 3; ++i) {
if (histogram.data_[i] > 0) {
if (count < 2) symbols[count] = i;
++count;
}
}
if (count <= 2 && symbols[0] < 256 && symbols[1] < 256) {
return ((symbols[0] <= 1 ? 4 : 11) +
(count == 2 ? 8 + histogram.total_count_ : 0));
}
uint8_t depth[kSize] = { 0 };
CreateHuffmanTree(&histogram.data_[0], kSize, 15, depth);
int bits = HuffmanBitCost(depth, kSize);
for (int i = 0; i < kSize; ++i) {
bits += histogram.data_[i] * depth[i];
}
return bits;
}
} // namespace brotli
#endif // BROTLI_ENC_BIT_COST_H_
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