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godot/drivers/webp/dsp/lossless_enc.c

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-Missing files in new webp version
2015-12-04 13:21:22 +00:00
// Copyright 2015 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.
// -----------------------------------------------------------------------------
//
// Image transform methods for lossless encoder.
//
// Authors: Vikas Arora (vikaas.arora@gmail.com)
// Jyrki Alakuijala (jyrki@google.com)
// Urvang Joshi (urvang@google.com)
#include "./dsp.h"
#include <math.h>
#include <stdlib.h>
#include "../dec/vp8li.h"
#include "../utils/endian_inl.h"
#include "./lossless.h"
#include "./yuv.h"
#define MAX_DIFF_COST (1e30f)
// lookup table for small values of log2(int)
const float kLog2Table[LOG_LOOKUP_IDX_MAX] = {
0.0000000000000000f, 0.0000000000000000f,
1.0000000000000000f, 1.5849625007211560f,
2.0000000000000000f, 2.3219280948873621f,
2.5849625007211560f, 2.8073549220576041f,
3.0000000000000000f, 3.1699250014423121f,
3.3219280948873621f, 3.4594316186372973f,
3.5849625007211560f, 3.7004397181410921f,
3.8073549220576041f, 3.9068905956085187f,
4.0000000000000000f, 4.0874628412503390f,
4.1699250014423121f, 4.2479275134435852f,
4.3219280948873626f, 4.3923174227787606f,
4.4594316186372973f, 4.5235619560570130f,
4.5849625007211560f, 4.6438561897747243f,
4.7004397181410917f, 4.7548875021634682f,
4.8073549220576037f, 4.8579809951275718f,
4.9068905956085187f, 4.9541963103868749f,
5.0000000000000000f, 5.0443941193584533f,
5.0874628412503390f, 5.1292830169449663f,
5.1699250014423121f, 5.2094533656289501f,
5.2479275134435852f, 5.2854022188622487f,
5.3219280948873626f, 5.3575520046180837f,
5.3923174227787606f, 5.4262647547020979f,
5.4594316186372973f, 5.4918530963296747f,
5.5235619560570130f, 5.5545888516776376f,
5.5849625007211560f, 5.6147098441152083f,
5.6438561897747243f, 5.6724253419714951f,
5.7004397181410917f, 5.7279204545631987f,
5.7548875021634682f, 5.7813597135246599f,
5.8073549220576037f, 5.8328900141647412f,
5.8579809951275718f, 5.8826430493618415f,
5.9068905956085187f, 5.9307373375628866f,
5.9541963103868749f, 5.9772799234999167f,
6.0000000000000000f, 6.0223678130284543f,
6.0443941193584533f, 6.0660891904577720f,
6.0874628412503390f, 6.1085244567781691f,
6.1292830169449663f, 6.1497471195046822f,
6.1699250014423121f, 6.1898245588800175f,
6.2094533656289501f, 6.2288186904958804f,
6.2479275134435852f, 6.2667865406949010f,
6.2854022188622487f, 6.3037807481771030f,
6.3219280948873626f, 6.3398500028846243f,
6.3575520046180837f, 6.3750394313469245f,
6.3923174227787606f, 6.4093909361377017f,
6.4262647547020979f, 6.4429434958487279f,
6.4594316186372973f, 6.4757334309663976f,
6.4918530963296747f, 6.5077946401986963f,
6.5235619560570130f, 6.5391588111080309f,
6.5545888516776376f, 6.5698556083309478f,
6.5849625007211560f, 6.5999128421871278f,
6.6147098441152083f, 6.6293566200796094f,
6.6438561897747243f, 6.6582114827517946f,
6.6724253419714951f, 6.6865005271832185f,
6.7004397181410917f, 6.7142455176661224f,
6.7279204545631987f, 6.7414669864011464f,
6.7548875021634682f, 6.7681843247769259f,
6.7813597135246599f, 6.7944158663501061f,
6.8073549220576037f, 6.8201789624151878f,
6.8328900141647412f, 6.8454900509443747f,
6.8579809951275718f, 6.8703647195834047f,
6.8826430493618415f, 6.8948177633079437f,
6.9068905956085187f, 6.9188632372745946f,
6.9307373375628866f, 6.9425145053392398f,
6.9541963103868749f, 6.9657842846620869f,
6.9772799234999167f, 6.9886846867721654f,
7.0000000000000000f, 7.0112272554232539f,
7.0223678130284543f, 7.0334230015374501f,
7.0443941193584533f, 7.0552824355011898f,
7.0660891904577720f, 7.0768155970508308f,
7.0874628412503390f, 7.0980320829605263f,
7.1085244567781691f, 7.1189410727235076f,
7.1292830169449663f, 7.1395513523987936f,
7.1497471195046822f, 7.1598713367783890f,
7.1699250014423121f, 7.1799090900149344f,
7.1898245588800175f, 7.1996723448363644f,
7.2094533656289501f, 7.2191685204621611f,
7.2288186904958804f, 7.2384047393250785f,
7.2479275134435852f, 7.2573878426926521f,
7.2667865406949010f, 7.2761244052742375f,
7.2854022188622487f, 7.2946207488916270f,
7.3037807481771030f, 7.3128829552843557f,
7.3219280948873626f, 7.3309168781146167f,
7.3398500028846243f, 7.3487281542310771f,
7.3575520046180837f, 7.3663222142458160f,
7.3750394313469245f, 7.3837042924740519f,
7.3923174227787606f, 7.4008794362821843f,
7.4093909361377017f, 7.4178525148858982f,
7.4262647547020979f, 7.4346282276367245f,
7.4429434958487279f, 7.4512111118323289f,
7.4594316186372973f, 7.4676055500829976f,
7.4757334309663976f, 7.4838157772642563f,
7.4918530963296747f, 7.4998458870832056f,
7.5077946401986963f, 7.5156998382840427f,
7.5235619560570130f, 7.5313814605163118f,
7.5391588111080309f, 7.5468944598876364f,
7.5545888516776376f, 7.5622424242210728f,
7.5698556083309478f, 7.5774288280357486f,
7.5849625007211560f, 7.5924570372680806f,
7.5999128421871278f, 7.6073303137496104f,
7.6147098441152083f, 7.6220518194563764f,
7.6293566200796094f, 7.6366246205436487f,
7.6438561897747243f, 7.6510516911789281f,
7.6582114827517946f, 7.6653359171851764f,
7.6724253419714951f, 7.6794800995054464f,
7.6865005271832185f, 7.6934869574993252f,
7.7004397181410917f, 7.7073591320808825f,
7.7142455176661224f, 7.7210991887071855f,
7.7279204545631987f, 7.7347096202258383f,
7.7414669864011464f, 7.7481928495894605f,
7.7548875021634682f, 7.7615512324444795f,
7.7681843247769259f, 7.7747870596011736f,
7.7813597135246599f, 7.7879025593914317f,
7.7944158663501061f, 7.8008998999203047f,
7.8073549220576037f, 7.8137811912170374f,
7.8201789624151878f, 7.8265484872909150f,
7.8328900141647412f, 7.8392037880969436f,
7.8454900509443747f, 7.8517490414160571f,
7.8579809951275718f, 7.8641861446542797f,
7.8703647195834047f, 7.8765169465649993f,
7.8826430493618415f, 7.8887432488982591f,
7.8948177633079437f, 7.9008668079807486f,
7.9068905956085187f, 7.9128893362299619f,
7.9188632372745946f, 7.9248125036057812f,
7.9307373375628866f, 7.9366379390025709f,
7.9425145053392398f, 7.9483672315846778f,
7.9541963103868749f, 7.9600019320680805f,
7.9657842846620869f, 7.9715435539507719f,
7.9772799234999167f, 7.9829935746943103f,
7.9886846867721654f, 7.9943534368588577f
};
const float kSLog2Table[LOG_LOOKUP_IDX_MAX] = {
0.00000000f, 0.00000000f, 2.00000000f, 4.75488750f,
8.00000000f, 11.60964047f, 15.50977500f, 19.65148445f,
24.00000000f, 28.52932501f, 33.21928095f, 38.05374781f,
43.01955001f, 48.10571634f, 53.30296891f, 58.60335893f,
64.00000000f, 69.48686830f, 75.05865003f, 80.71062276f,
86.43856190f, 92.23866588f, 98.10749561f, 104.04192499f,
110.03910002f, 116.09640474f, 122.21143267f, 128.38196256f,
134.60593782f, 140.88144886f, 147.20671787f, 153.58008562f,
160.00000000f, 166.46500594f, 172.97373660f, 179.52490559f,
186.11730005f, 192.74977453f, 199.42124551f, 206.13068654f,
212.87712380f, 219.65963219f, 226.47733176f, 233.32938445f,
240.21499122f, 247.13338933f, 254.08384998f, 261.06567603f,
268.07820003f, 275.12078236f, 282.19280949f, 289.29369244f,
296.42286534f, 303.57978409f, 310.76392512f, 317.97478424f,
325.21187564f, 332.47473081f, 339.76289772f, 347.07593991f,
354.41343574f, 361.77497759f, 369.16017124f, 376.56863518f,
384.00000000f, 391.45390785f, 398.93001188f, 406.42797576f,
413.94747321f, 421.48818752f, 429.04981119f, 436.63204548f,
444.23460010f, 451.85719280f, 459.49954906f, 467.16140179f,
474.84249102f, 482.54256363f, 490.26137307f, 497.99867911f,
505.75424759f, 513.52785023f, 521.31926438f, 529.12827280f,
536.95466351f, 544.79822957f, 552.65876890f, 560.53608414f,
568.42998244f, 576.34027536f, 584.26677867f, 592.20931226f,
600.16769996f, 608.14176943f, 616.13135206f, 624.13628279f,
632.15640007f, 640.19154569f, 648.24156472f, 656.30630539f,
664.38561898f, 672.47935976f, 680.58738488f, 688.70955430f,
696.84573069f, 704.99577935f, 713.15956818f, 721.33696754f,
729.52785023f, 737.73209140f, 745.94956849f, 754.18016116f,
762.42375127f, 770.68022275f, 778.94946161f, 787.23135586f,
795.52579543f, 803.83267219f, 812.15187982f, 820.48331383f,
828.82687147f, 837.18245171f, 845.54995518f, 853.92928416f,
862.32034249f, 870.72303558f, 879.13727036f, 887.56295522f,
896.00000000f, 904.44831595f, 912.90781569f, 921.37841320f,
929.86002376f, 938.35256392f, 946.85595152f, 955.37010560f,
963.89494641f, 972.43039537f, 980.97637504f, 989.53280911f,
998.09962237f, 1006.67674069f, 1015.26409097f, 1023.86160116f,
1032.46920021f, 1041.08681805f, 1049.71438560f, 1058.35183469f,
1066.99909811f, 1075.65610955f, 1084.32280357f, 1092.99911564f,
1101.68498204f, 1110.38033993f, 1119.08512727f, 1127.79928282f,
1136.52274614f, 1145.25545758f, 1153.99735821f, 1162.74838989f,
1171.50849518f, 1180.27761738f, 1189.05570047f, 1197.84268914f,
1206.63852876f, 1215.44316535f, 1224.25654560f, 1233.07861684f,
1241.90932703f, 1250.74862473f, 1259.59645914f, 1268.45278005f,
1277.31753781f, 1286.19068338f, 1295.07216828f, 1303.96194457f,
1312.85996488f, 1321.76618236f, 1330.68055071f, 1339.60302413f,
1348.53355734f, 1357.47210556f, 1366.41862452f, 1375.37307041f,
1384.33539991f, 1393.30557020f, 1402.28353887f, 1411.26926400f,
1420.26270412f, 1429.26381818f, 1438.27256558f, 1447.28890615f,
1456.31280014f, 1465.34420819f, 1474.38309138f, 1483.42941118f,
1492.48312945f, 1501.54420843f, 1510.61261078f, 1519.68829949f,
1528.77123795f, 1537.86138993f, 1546.95871952f, 1556.06319119f,
1565.17476976f, 1574.29342040f, 1583.41910860f, 1592.55180020f,
1601.69146137f, 1610.83805860f, 1619.99155871f, 1629.15192882f,
1638.31913637f, 1647.49314911f, 1656.67393509f, 1665.86146266f,
1675.05570047f, 1684.25661744f, 1693.46418280f, 1702.67836605f,
1711.89913698f, 1721.12646563f, 1730.36032233f, 1739.60067768f,
1748.84750254f, 1758.10076802f, 1767.36044551f, 1776.62650662f,
1785.89892323f, 1795.17766747f, 1804.46271172f, 1813.75402857f,
1823.05159087f, 1832.35537170f, 1841.66534438f, 1850.98148244f,
1860.30375965f, 1869.63214999f, 1878.96662767f, 1888.30716711f,
1897.65374295f, 1907.00633003f, 1916.36490342f, 1925.72943838f,
1935.09991037f, 1944.47629506f, 1953.85856831f, 1963.24670620f,
1972.64068498f, 1982.04048108f, 1991.44607117f, 2000.85743204f,
2010.27454072f, 2019.69737440f, 2029.12591044f, 2038.56012640f
};
const VP8LPrefixCode kPrefixEncodeCode[PREFIX_LOOKUP_IDX_MAX] = {
{ 0, 0}, { 0, 0}, { 1, 0}, { 2, 0}, { 3, 0}, { 4, 1}, { 4, 1}, { 5, 1},
{ 5, 1}, { 6, 2}, { 6, 2}, { 6, 2}, { 6, 2}, { 7, 2}, { 7, 2}, { 7, 2},
{ 7, 2}, { 8, 3}, { 8, 3}, { 8, 3}, { 8, 3}, { 8, 3}, { 8, 3}, { 8, 3},
{ 8, 3}, { 9, 3}, { 9, 3}, { 9, 3}, { 9, 3}, { 9, 3}, { 9, 3}, { 9, 3},
{ 9, 3}, {10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4},
{10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4}, {10, 4},
{10, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4},
{11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4}, {11, 4},
{11, 4}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5},
{12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5},
{12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5},
{12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5}, {12, 5},
{12, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5},
{13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5},
{13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5},
{13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5}, {13, 5},
{13, 5}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6}, {14, 6},
{14, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6}, {15, 6},
{15, 6}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7}, {16, 7},
{16, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
{17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7}, {17, 7},
};
const uint8_t kPrefixEncodeExtraBitsValue[PREFIX_LOOKUP_IDX_MAX] = {
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 2, 3, 0, 1, 2, 3,
0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126
};
// The threshold till approximate version of log_2 can be used.
// Practically, we can get rid of the call to log() as the two values match to
// very high degree (the ratio of these two is 0.99999x).
// Keeping a high threshold for now.
#define APPROX_LOG_WITH_CORRECTION_MAX 65536
#define APPROX_LOG_MAX 4096
#define LOG_2_RECIPROCAL 1.44269504088896338700465094007086
static float FastSLog2Slow(uint32_t v) {
assert(v >= LOG_LOOKUP_IDX_MAX);
if (v < APPROX_LOG_WITH_CORRECTION_MAX) {
int log_cnt = 0;
uint32_t y = 1;
int correction = 0;
const float v_f = (float)v;
const uint32_t orig_v = v;
do {
++log_cnt;
v = v >> 1;
y = y << 1;
} while (v >= LOG_LOOKUP_IDX_MAX);
// vf = (2^log_cnt) * Xf; where y = 2^log_cnt and Xf < 256
// Xf = floor(Xf) * (1 + (v % y) / v)
// log2(Xf) = log2(floor(Xf)) + log2(1 + (v % y) / v)
// The correction factor: log(1 + d) ~ d; for very small d values, so
// log2(1 + (v % y) / v) ~ LOG_2_RECIPROCAL * (v % y)/v
// LOG_2_RECIPROCAL ~ 23/16
correction = (23 * (orig_v & (y - 1))) >> 4;
return v_f * (kLog2Table[v] + log_cnt) + correction;
} else {
return (float)(LOG_2_RECIPROCAL * v * log((double)v));
}
}
static float FastLog2Slow(uint32_t v) {
assert(v >= LOG_LOOKUP_IDX_MAX);
if (v < APPROX_LOG_WITH_CORRECTION_MAX) {
int log_cnt = 0;
uint32_t y = 1;
const uint32_t orig_v = v;
double log_2;
do {
++log_cnt;
v = v >> 1;
y = y << 1;
} while (v >= LOG_LOOKUP_IDX_MAX);
log_2 = kLog2Table[v] + log_cnt;
if (orig_v >= APPROX_LOG_MAX) {
// Since the division is still expensive, add this correction factor only
// for large values of 'v'.
const int correction = (23 * (orig_v & (y - 1))) >> 4;
log_2 += (double)correction / orig_v;
}
return (float)log_2;
} else {
return (float)(LOG_2_RECIPROCAL * log((double)v));
}
}
// Mostly used to reduce code size + readability
static WEBP_INLINE int GetMin(int a, int b) { return (a > b) ? b : a; }
//------------------------------------------------------------------------------
// Methods to calculate Entropy (Shannon).
static float PredictionCostSpatial(const int counts[256], int weight_0,
double exp_val) {
const int significant_symbols = 256 >> 4;
const double exp_decay_factor = 0.6;
double bits = weight_0 * counts[0];
int i;
for (i = 1; i < significant_symbols; ++i) {
bits += exp_val * (counts[i] + counts[256 - i]);
exp_val *= exp_decay_factor;
}
return (float)(-0.1 * bits);
}
// Compute the combined Shanon's entropy for distribution {X} and {X+Y}
static float CombinedShannonEntropy(const int X[256], const int Y[256]) {
int i;
double retval = 0.;
int sumX = 0, sumXY = 0;
for (i = 0; i < 256; ++i) {
const int x = X[i];
const int xy = x + Y[i];
if (x != 0) {
sumX += x;
retval -= VP8LFastSLog2(x);
sumXY += xy;
retval -= VP8LFastSLog2(xy);
} else if (xy != 0) {
sumXY += xy;
retval -= VP8LFastSLog2(xy);
}
}
retval += VP8LFastSLog2(sumX) + VP8LFastSLog2(sumXY);
return (float)retval;
}
static float PredictionCostSpatialHistogram(const int accumulated[4][256],
const int tile[4][256]) {
int i;
double retval = 0;
for (i = 0; i < 4; ++i) {
const double kExpValue = 0.94;
retval += PredictionCostSpatial(tile[i], 1, kExpValue);
retval += CombinedShannonEntropy(tile[i], accumulated[i]);
}
return (float)retval;
}
static WEBP_INLINE double BitsEntropyRefine(int nonzeros, int sum, int max_val,
double retval) {
double mix;
if (nonzeros < 5) {
if (nonzeros <= 1) {
return 0;
}
// Two symbols, they will be 0 and 1 in a Huffman code.
// Let's mix in a bit of entropy to favor good clustering when
// distributions of these are combined.
if (nonzeros == 2) {
return 0.99 * sum + 0.01 * retval;
}
// No matter what the entropy says, we cannot be better than min_limit
// with Huffman coding. I am mixing a bit of entropy into the
// min_limit since it produces much better (~0.5 %) compression results
// perhaps because of better entropy clustering.
if (nonzeros == 3) {
mix = 0.95;
} else {
mix = 0.7; // nonzeros == 4.
}
} else {
mix = 0.627;
}
{
double min_limit = 2 * sum - max_val;
min_limit = mix * min_limit + (1.0 - mix) * retval;
return (retval < min_limit) ? min_limit : retval;
}
}
// Returns the entropy for the symbols in the input array.
// Also sets trivial_symbol to the code value, if the array has only one code
// value. Otherwise, set it to VP8L_NON_TRIVIAL_SYM.
double VP8LBitsEntropy(const uint32_t* const array, int n,
uint32_t* const trivial_symbol) {
double retval = 0.;
uint32_t sum = 0;
uint32_t nonzero_code = VP8L_NON_TRIVIAL_SYM;
int nonzeros = 0;
uint32_t max_val = 0;
int i;
for (i = 0; i < n; ++i) {
if (array[i] != 0) {
sum += array[i];
nonzero_code = i;
++nonzeros;
retval -= VP8LFastSLog2(array[i]);
if (max_val < array[i]) {
max_val = array[i];
}
}
}
retval += VP8LFastSLog2(sum);
if (trivial_symbol != NULL) {
*trivial_symbol = (nonzeros == 1) ? nonzero_code : VP8L_NON_TRIVIAL_SYM;
}
return BitsEntropyRefine(nonzeros, sum, max_val, retval);
}
static double InitialHuffmanCost(void) {
// Small bias because Huffman code length is typically not stored in
// full length.
static const int kHuffmanCodeOfHuffmanCodeSize = CODE_LENGTH_CODES * 3;
static const double kSmallBias = 9.1;
return kHuffmanCodeOfHuffmanCodeSize - kSmallBias;
}
// Finalize the Huffman cost based on streak numbers and length type (<3 or >=3)
static double FinalHuffmanCost(const VP8LStreaks* const stats) {
double retval = InitialHuffmanCost();
retval += stats->counts[0] * 1.5625 + 0.234375 * stats->streaks[0][1];
retval += stats->counts[1] * 2.578125 + 0.703125 * stats->streaks[1][1];
retval += 1.796875 * stats->streaks[0][0];
retval += 3.28125 * stats->streaks[1][0];
return retval;
}
// Trampolines
static double HuffmanCost(const uint32_t* const population, int length) {
const VP8LStreaks stats = VP8LHuffmanCostCount(population, length);
return FinalHuffmanCost(&stats);
}
// Aggregated costs
double VP8LPopulationCost(const uint32_t* const population, int length,
uint32_t* const trivial_sym) {
return
VP8LBitsEntropy(population, length, trivial_sym) +
HuffmanCost(population, length);
}
double VP8LGetCombinedEntropy(const uint32_t* const X,
const uint32_t* const Y, int length) {
double bits_entropy_combined;
double huffman_cost_combined;
int i;
// Bit entropy variables.
double retval = 0.;
int sum = 0;
int nonzeros = 0;
uint32_t max_val = 0;
int i_prev;
uint32_t xy;
// Huffman cost variables.
int streak = 0;
uint32_t xy_prev;
VP8LStreaks stats;
memset(&stats, 0, sizeof(stats));
// Treat the first value for the huffman cost: this is keeping the original
// behavior, even though there is no first streak.
// TODO(vrabaud): study proper behavior
xy = X[0] + Y[0];
++stats.streaks[xy != 0][0];
xy_prev = xy;
i_prev = 0;
for (i = 1; i < length; ++i) {
xy = X[i] + Y[i];
// Process data by streaks for both bit entropy and huffman cost.
if (xy != xy_prev) {
streak = i - i_prev;
// Gather info for the bit entropy.
if (xy_prev != 0) {
sum += xy_prev * streak;
nonzeros += streak;
retval -= VP8LFastSLog2(xy_prev) * streak;
if (max_val < xy_prev) {
max_val = xy_prev;
}
}
// Gather info for the huffman cost.
stats.counts[xy != 0] += (streak > 3);
stats.streaks[xy != 0][(streak > 3)] += streak;
xy_prev = xy;
i_prev = i;
}
}
// Finish off the last streak for bit entropy.
if (xy != 0) {
streak = i - i_prev;
sum += xy * streak;
nonzeros += streak;
retval -= VP8LFastSLog2(xy) * streak;
if (max_val < xy) {
max_val = xy;
}
}
// Huffman cost is not updated with the last streak to keep original behavior.
// TODO(vrabaud): study proper behavior
retval += VP8LFastSLog2(sum);
bits_entropy_combined = BitsEntropyRefine(nonzeros, sum, max_val, retval);
huffman_cost_combined = FinalHuffmanCost(&stats);
return bits_entropy_combined + huffman_cost_combined;
}
// Estimates the Entropy + Huffman + other block overhead size cost.
double VP8LHistogramEstimateBits(const VP8LHistogram* const p) {
return
VP8LPopulationCost(
p->literal_, VP8LHistogramNumCodes(p->palette_code_bits_), NULL)
+ VP8LPopulationCost(p->red_, NUM_LITERAL_CODES, NULL)
+ VP8LPopulationCost(p->blue_, NUM_LITERAL_CODES, NULL)
+ VP8LPopulationCost(p->alpha_, NUM_LITERAL_CODES, NULL)
+ VP8LPopulationCost(p->distance_, NUM_DISTANCE_CODES, NULL)
+ VP8LExtraCost(p->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES)
+ VP8LExtraCost(p->distance_, NUM_DISTANCE_CODES);
}
double VP8LHistogramEstimateBitsBulk(const VP8LHistogram* const p) {
return
VP8LBitsEntropy(p->literal_, VP8LHistogramNumCodes(p->palette_code_bits_),
NULL)
+ VP8LBitsEntropy(p->red_, NUM_LITERAL_CODES, NULL)
+ VP8LBitsEntropy(p->blue_, NUM_LITERAL_CODES, NULL)
+ VP8LBitsEntropy(p->alpha_, NUM_LITERAL_CODES, NULL)
+ VP8LBitsEntropy(p->distance_, NUM_DISTANCE_CODES, NULL)
+ VP8LExtraCost(p->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES)
+ VP8LExtraCost(p->distance_, NUM_DISTANCE_CODES);
}
static WEBP_INLINE void UpdateHisto(int histo_argb[4][256], uint32_t argb) {
++histo_argb[0][argb >> 24];
++histo_argb[1][(argb >> 16) & 0xff];
++histo_argb[2][(argb >> 8) & 0xff];
++histo_argb[3][argb & 0xff];
}
//------------------------------------------------------------------------------
// Returns best predictor and updates the accumulated histogram.
static int GetBestPredictorForTile(int width, int height,
int tile_x, int tile_y, int bits,
int accumulated[4][256],
const uint32_t* const argb_scratch) {
const int kNumPredModes = 14;
const int col_start = tile_x << bits;
const int row_start = tile_y << bits;
const int tile_size = 1 << bits;
const int max_y = GetMin(tile_size, height - row_start);
const int max_x = GetMin(tile_size, width - col_start);
float best_diff = MAX_DIFF_COST;
int best_mode = 0;
int mode;
int histo_stack_1[4][256];
int histo_stack_2[4][256];
// Need pointers to be able to swap arrays.
int (*histo_argb)[256] = histo_stack_1;
int (*best_histo)[256] = histo_stack_2;
int i, j;
for (mode = 0; mode < kNumPredModes; ++mode) {
const uint32_t* current_row = argb_scratch;
const VP8LPredictorFunc pred_func = VP8LPredictors[mode];
float cur_diff;
int y;
memset(histo_argb, 0, sizeof(histo_stack_1));
for (y = 0; y < max_y; ++y) {
int x;
const int row = row_start + y;
const uint32_t* const upper_row = current_row;
current_row = upper_row + width;
for (x = 0; x < max_x; ++x) {
const int col = col_start + x;
uint32_t predict;
if (row == 0) {
predict = (col == 0) ? ARGB_BLACK : current_row[col - 1]; // Left.
} else if (col == 0) {
predict = upper_row[col]; // Top.
} else {
predict = pred_func(current_row[col - 1], upper_row + col);
}
UpdateHisto(histo_argb, VP8LSubPixels(current_row[col], predict));
}
}
cur_diff = PredictionCostSpatialHistogram(
(const int (*)[256])accumulated, (const int (*)[256])histo_argb);
if (cur_diff < best_diff) {
int (*tmp)[256] = histo_argb;
histo_argb = best_histo;
best_histo = tmp;
best_diff = cur_diff;
best_mode = mode;
}
}
for (i = 0; i < 4; i++) {
for (j = 0; j < 256; j++) {
accumulated[i][j] += best_histo[i][j];
}
}
return best_mode;
}
static void CopyImageWithPrediction(int width, int height,
int bits, uint32_t* const modes,
uint32_t* const argb_scratch,
uint32_t* const argb) {
const int tiles_per_row = VP8LSubSampleSize(width, bits);
const int mask = (1 << bits) - 1;
// The row size is one pixel longer to allow the top right pixel to point to
// the leftmost pixel of the next row when at the right edge.
uint32_t* current_row = argb_scratch;
uint32_t* upper_row = argb_scratch + width + 1;
int y;
VP8LPredictorFunc pred_func = 0;
for (y = 0; y < height; ++y) {
int x;
uint32_t* tmp = upper_row;
upper_row = current_row;
current_row = tmp;
memcpy(current_row, argb + y * width, sizeof(*current_row) * width);
current_row[width] = (y + 1 < height) ? argb[(y + 1) * width] : ARGB_BLACK;
for (x = 0; x < width; ++x) {
uint32_t predict;
if ((x & mask) == 0) {
const int mode =
(modes[(y >> bits) * tiles_per_row + (x >> bits)] >> 8) & 0xff;
pred_func = VP8LPredictors[mode];
}
if (y == 0) {
predict = (x == 0) ? ARGB_BLACK : current_row[x - 1]; // Left.
} else if (x == 0) {
predict = upper_row[x]; // Top.
} else {
predict = pred_func(current_row[x - 1], upper_row + x);
}
argb[y * width + x] = VP8LSubPixels(current_row[x], predict);
}
}
}
void VP8LResidualImage(int width, int height, int bits, int low_effort,
uint32_t* const argb, uint32_t* const argb_scratch,
uint32_t* const image) {
const int max_tile_size = 1 << bits;
const int tiles_per_row = VP8LSubSampleSize(width, bits);
const int tiles_per_col = VP8LSubSampleSize(height, bits);
const int kPredLowEffort = 11;
uint32_t* const upper_row = argb_scratch;
uint32_t* const current_tile_rows = argb_scratch + width;
int tile_y;
int histo[4][256];
if (!low_effort) memset(histo, 0, sizeof(histo));
for (tile_y = 0; tile_y < tiles_per_col; ++tile_y) {
const int tile_y_offset = tile_y * max_tile_size;
const int this_tile_height =
(tile_y < tiles_per_col - 1) ? max_tile_size : height - tile_y_offset;
int tile_x;
if (tile_y > 0) {
memcpy(upper_row, current_tile_rows + (max_tile_size - 1) * width,
width * sizeof(*upper_row));
}
memcpy(current_tile_rows, &argb[tile_y_offset * width],
this_tile_height * width * sizeof(*current_tile_rows));
for (tile_x = 0; tile_x < tiles_per_row; ++tile_x) {
const int pred =
low_effort ? kPredLowEffort :
GetBestPredictorForTile(width, height,
tile_x, tile_y, bits,
(int (*)[256])histo,
argb_scratch);
image[tile_y * tiles_per_row + tile_x] = 0xff000000u | (pred << 8);
}
}
CopyImageWithPrediction(width, height, bits, image, argb_scratch, argb);
}
void VP8LSubtractGreenFromBlueAndRed_C(uint32_t* argb_data, int num_pixels) {
int i;
for (i = 0; i < num_pixels; ++i) {
const uint32_t argb = argb_data[i];
const uint32_t green = (argb >> 8) & 0xff;
const uint32_t new_r = (((argb >> 16) & 0xff) - green) & 0xff;
const uint32_t new_b = ((argb & 0xff) - green) & 0xff;
argb_data[i] = (argb & 0xff00ff00) | (new_r << 16) | new_b;
}
}
static WEBP_INLINE void MultipliersClear(VP8LMultipliers* const m) {
m->green_to_red_ = 0;
m->green_to_blue_ = 0;
m->red_to_blue_ = 0;
}
static WEBP_INLINE uint32_t ColorTransformDelta(int8_t color_pred,
int8_t color) {
return (uint32_t)((int)(color_pred) * color) >> 5;
}
static WEBP_INLINE void ColorCodeToMultipliers(uint32_t color_code,
VP8LMultipliers* const m) {
m->green_to_red_ = (color_code >> 0) & 0xff;
m->green_to_blue_ = (color_code >> 8) & 0xff;
m->red_to_blue_ = (color_code >> 16) & 0xff;
}
static WEBP_INLINE uint32_t MultipliersToColorCode(
const VP8LMultipliers* const m) {
return 0xff000000u |
((uint32_t)(m->red_to_blue_) << 16) |
((uint32_t)(m->green_to_blue_) << 8) |
m->green_to_red_;
}
void VP8LTransformColor_C(const VP8LMultipliers* const m, uint32_t* data,
int num_pixels) {
int i;
for (i = 0; i < num_pixels; ++i) {
const uint32_t argb = data[i];
const uint32_t green = argb >> 8;
const uint32_t red = argb >> 16;
uint32_t new_red = red;
uint32_t new_blue = argb;
new_red -= ColorTransformDelta(m->green_to_red_, green);
new_red &= 0xff;
new_blue -= ColorTransformDelta(m->green_to_blue_, green);
new_blue -= ColorTransformDelta(m->red_to_blue_, red);
new_blue &= 0xff;
data[i] = (argb & 0xff00ff00u) | (new_red << 16) | (new_blue);
}
}
static WEBP_INLINE uint8_t TransformColorRed(uint8_t green_to_red,
uint32_t argb) {
const uint32_t green = argb >> 8;
uint32_t new_red = argb >> 16;
new_red -= ColorTransformDelta(green_to_red, green);
return (new_red & 0xff);
}
static WEBP_INLINE uint8_t TransformColorBlue(uint8_t green_to_blue,
uint8_t red_to_blue,
uint32_t argb) {
const uint32_t green = argb >> 8;
const uint32_t red = argb >> 16;
uint8_t new_blue = argb;
new_blue -= ColorTransformDelta(green_to_blue, green);
new_blue -= ColorTransformDelta(red_to_blue, red);
return (new_blue & 0xff);
}
static float PredictionCostCrossColor(const int accumulated[256],
const int counts[256]) {
// Favor low entropy, locally and globally.
// Favor small absolute values for PredictionCostSpatial
static const double kExpValue = 2.4;
return CombinedShannonEntropy(counts, accumulated) +
PredictionCostSpatial(counts, 3, kExpValue);
}
void VP8LCollectColorRedTransforms_C(const uint32_t* argb, int stride,
int tile_width, int tile_height,
int green_to_red, int histo[]) {
while (tile_height-- > 0) {
int x;
for (x = 0; x < tile_width; ++x) {
++histo[TransformColorRed(green_to_red, argb[x])];
}
argb += stride;
}
}
static float GetPredictionCostCrossColorRed(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y, int green_to_red,
const int accumulated_red_histo[256]) {
int histo[256] = { 0 };
float cur_diff;
VP8LCollectColorRedTransforms(argb, stride, tile_width, tile_height,
green_to_red, histo);
cur_diff = PredictionCostCrossColor(accumulated_red_histo, histo);
if ((uint8_t)green_to_red == prev_x.green_to_red_) {
cur_diff -= 3; // favor keeping the areas locally similar
}
if ((uint8_t)green_to_red == prev_y.green_to_red_) {
cur_diff -= 3; // favor keeping the areas locally similar
}
if (green_to_red == 0) {
cur_diff -= 3;
}
return cur_diff;
}
static void GetBestGreenToRed(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y, int quality,
const int accumulated_red_histo[256], VP8LMultipliers* const best_tx) {
const int kMaxIters = 4 + ((7 * quality) >> 8); // in range [4..6]
int green_to_red_best = 0;
int iter, offset;
float best_diff = GetPredictionCostCrossColorRed(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_red_best, accumulated_red_histo);
for (iter = 0; iter < kMaxIters; ++iter) {
// ColorTransformDelta is a 3.5 bit fixed point, so 32 is equal to
// one in color computation. Having initial delta here as 1 is sufficient
// to explore the range of (-2, 2).
const int delta = 32 >> iter;
// Try a negative and a positive delta from the best known value.
for (offset = -delta; offset <= delta; offset += 2 * delta) {
const int green_to_red_cur = offset + green_to_red_best;
const float cur_diff = GetPredictionCostCrossColorRed(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_red_cur, accumulated_red_histo);
if (cur_diff < best_diff) {
best_diff = cur_diff;
green_to_red_best = green_to_red_cur;
}
}
}
best_tx->green_to_red_ = green_to_red_best;
}
void VP8LCollectColorBlueTransforms_C(const uint32_t* argb, int stride,
int tile_width, int tile_height,
int green_to_blue, int red_to_blue,
int histo[]) {
while (tile_height-- > 0) {
int x;
for (x = 0; x < tile_width; ++x) {
++histo[TransformColorBlue(green_to_blue, red_to_blue, argb[x])];
}
argb += stride;
}
}
static float GetPredictionCostCrossColorBlue(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y,
int green_to_blue, int red_to_blue, const int accumulated_blue_histo[256]) {
int histo[256] = { 0 };
float cur_diff;
VP8LCollectColorBlueTransforms(argb, stride, tile_width, tile_height,
green_to_blue, red_to_blue, histo);
cur_diff = PredictionCostCrossColor(accumulated_blue_histo, histo);
if ((uint8_t)green_to_blue == prev_x.green_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
}
if ((uint8_t)green_to_blue == prev_y.green_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
}
if ((uint8_t)red_to_blue == prev_x.red_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
}
if ((uint8_t)red_to_blue == prev_y.red_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
}
if (green_to_blue == 0) {
cur_diff -= 3;
}
if (red_to_blue == 0) {
cur_diff -= 3;
}
return cur_diff;
}
#define kGreenRedToBlueNumAxis 8
#define kGreenRedToBlueMaxIters 7
static void GetBestGreenRedToBlue(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y, int quality,
const int accumulated_blue_histo[256],
VP8LMultipliers* const best_tx) {
const int8_t offset[kGreenRedToBlueNumAxis][2] =
{{0, -1}, {0, 1}, {-1, 0}, {1, 0}, {-1, -1}, {-1, 1}, {1, -1}, {1, 1}};
const int8_t delta_lut[kGreenRedToBlueMaxIters] = { 16, 16, 8, 4, 2, 2, 2 };
const int iters =
(quality < 25) ? 1 : (quality > 50) ? kGreenRedToBlueMaxIters : 4;
int green_to_blue_best = 0;
int red_to_blue_best = 0;
int iter;
// Initial value at origin:
float best_diff = GetPredictionCostCrossColorBlue(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_blue_best, red_to_blue_best, accumulated_blue_histo);
for (iter = 0; iter < iters; ++iter) {
const int delta = delta_lut[iter];
int axis;
for (axis = 0; axis < kGreenRedToBlueNumAxis; ++axis) {
const int green_to_blue_cur =
offset[axis][0] * delta + green_to_blue_best;
const int red_to_blue_cur = offset[axis][1] * delta + red_to_blue_best;
const float cur_diff = GetPredictionCostCrossColorBlue(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_blue_cur, red_to_blue_cur, accumulated_blue_histo);
if (cur_diff < best_diff) {
best_diff = cur_diff;
green_to_blue_best = green_to_blue_cur;
red_to_blue_best = red_to_blue_cur;
}
if (quality < 25 && iter == 4) {
// Only axis aligned diffs for lower quality.
break; // next iter.
}
}
if (delta == 2 && green_to_blue_best == 0 && red_to_blue_best == 0) {
// Further iterations would not help.
break; // out of iter-loop.
}
}
best_tx->green_to_blue_ = green_to_blue_best;
best_tx->red_to_blue_ = red_to_blue_best;
}
#undef kGreenRedToBlueMaxIters
#undef kGreenRedToBlueNumAxis
static VP8LMultipliers GetBestColorTransformForTile(
int tile_x, int tile_y, int bits,
VP8LMultipliers prev_x,
VP8LMultipliers prev_y,
int quality, int xsize, int ysize,
const int accumulated_red_histo[256],
const int accumulated_blue_histo[256],
const uint32_t* const argb) {
const int max_tile_size = 1 << bits;
const int tile_y_offset = tile_y * max_tile_size;
const int tile_x_offset = tile_x * max_tile_size;
const int all_x_max = GetMin(tile_x_offset + max_tile_size, xsize);
const int all_y_max = GetMin(tile_y_offset + max_tile_size, ysize);
const int tile_width = all_x_max - tile_x_offset;
const int tile_height = all_y_max - tile_y_offset;
const uint32_t* const tile_argb = argb + tile_y_offset * xsize
+ tile_x_offset;
VP8LMultipliers best_tx;
MultipliersClear(&best_tx);
GetBestGreenToRed(tile_argb, xsize, tile_width, tile_height,
prev_x, prev_y, quality, accumulated_red_histo, &best_tx);
GetBestGreenRedToBlue(tile_argb, xsize, tile_width, tile_height,
prev_x, prev_y, quality, accumulated_blue_histo,
&best_tx);
return best_tx;
}
static void CopyTileWithColorTransform(int xsize, int ysize,
int tile_x, int tile_y,
int max_tile_size,
VP8LMultipliers color_transform,
uint32_t* argb) {
const int xscan = GetMin(max_tile_size, xsize - tile_x);
int yscan = GetMin(max_tile_size, ysize - tile_y);
argb += tile_y * xsize + tile_x;
while (yscan-- > 0) {
VP8LTransformColor(&color_transform, argb, xscan);
argb += xsize;
}
}
void VP8LColorSpaceTransform(int width, int height, int bits, int quality,
uint32_t* const argb, uint32_t* image) {
const int max_tile_size = 1 << bits;
const int tile_xsize = VP8LSubSampleSize(width, bits);
const int tile_ysize = VP8LSubSampleSize(height, bits);
int accumulated_red_histo[256] = { 0 };
int accumulated_blue_histo[256] = { 0 };
int tile_x, tile_y;
VP8LMultipliers prev_x, prev_y;
MultipliersClear(&prev_y);
MultipliersClear(&prev_x);
for (tile_y = 0; tile_y < tile_ysize; ++tile_y) {
for (tile_x = 0; tile_x < tile_xsize; ++tile_x) {
int y;
const int tile_x_offset = tile_x * max_tile_size;
const int tile_y_offset = tile_y * max_tile_size;
const int all_x_max = GetMin(tile_x_offset + max_tile_size, width);
const int all_y_max = GetMin(tile_y_offset + max_tile_size, height);
const int offset = tile_y * tile_xsize + tile_x;
if (tile_y != 0) {
ColorCodeToMultipliers(image[offset - tile_xsize], &prev_y);
}
prev_x = GetBestColorTransformForTile(tile_x, tile_y, bits,
prev_x, prev_y,
quality, width, height,
accumulated_red_histo,
accumulated_blue_histo,
argb);
image[offset] = MultipliersToColorCode(&prev_x);
CopyTileWithColorTransform(width, height, tile_x_offset, tile_y_offset,
max_tile_size, prev_x, argb);
// Gather accumulated histogram data.
for (y = tile_y_offset; y < all_y_max; ++y) {
int ix = y * width + tile_x_offset;
const int ix_end = ix + all_x_max - tile_x_offset;
for (; ix < ix_end; ++ix) {
const uint32_t pix = argb[ix];
if (ix >= 2 &&
pix == argb[ix - 2] &&
pix == argb[ix - 1]) {
continue; // repeated pixels are handled by backward references
}
if (ix >= width + 2 &&
argb[ix - 2] == argb[ix - width - 2] &&
argb[ix - 1] == argb[ix - width - 1] &&
pix == argb[ix - width]) {
continue; // repeated pixels are handled by backward references
}
++accumulated_red_histo[(pix >> 16) & 0xff];
++accumulated_blue_histo[(pix >> 0) & 0xff];
}
}
}
}
}
//------------------------------------------------------------------------------
// Bundles multiple (1, 2, 4 or 8) pixels into a single pixel.
void VP8LBundleColorMap(const uint8_t* const row, int width,
int xbits, uint32_t* const dst) {
int x;
if (xbits > 0) {
const int bit_depth = 1 << (3 - xbits);
const int mask = (1 << xbits) - 1;
uint32_t code = 0xff000000;
for (x = 0; x < width; ++x) {
const int xsub = x & mask;
if (xsub == 0) {
code = 0xff000000;
}
code |= row[x] << (8 + bit_depth * xsub);
dst[x >> xbits] = code;
}
} else {
for (x = 0; x < width; ++x) dst[x] = 0xff000000 | (row[x] << 8);
}
}
//------------------------------------------------------------------------------
static double ExtraCost(const uint32_t* population, int length) {
int i;
double cost = 0.;
for (i = 2; i < length - 2; ++i) cost += (i >> 1) * population[i + 2];
return cost;
}
static double ExtraCostCombined(const uint32_t* X, const uint32_t* Y,
int length) {
int i;
double cost = 0.;
for (i = 2; i < length - 2; ++i) {
const int xy = X[i + 2] + Y[i + 2];
cost += (i >> 1) * xy;
}
return cost;
}
// Returns the various RLE counts
static VP8LStreaks HuffmanCostCount(const uint32_t* population, int length) {
int i;
int streak = 0;
VP8LStreaks stats;
memset(&stats, 0, sizeof(stats));
for (i = 0; i < length - 1; ++i) {
++streak;
if (population[i] == population[i + 1]) {
continue;
}
stats.counts[population[i] != 0] += (streak > 3);
stats.streaks[population[i] != 0][(streak > 3)] += streak;
streak = 0;
}
++streak;
stats.counts[population[i] != 0] += (streak > 3);
stats.streaks[population[i] != 0][(streak > 3)] += streak;
return stats;
}
//------------------------------------------------------------------------------
static void HistogramAdd(const VP8LHistogram* const a,
const VP8LHistogram* const b,
VP8LHistogram* const out) {
int i;
const int literal_size = VP8LHistogramNumCodes(a->palette_code_bits_);
assert(a->palette_code_bits_ == b->palette_code_bits_);
if (b != out) {
for (i = 0; i < literal_size; ++i) {
out->literal_[i] = a->literal_[i] + b->literal_[i];
}
for (i = 0; i < NUM_DISTANCE_CODES; ++i) {
out->distance_[i] = a->distance_[i] + b->distance_[i];
}
for (i = 0; i < NUM_LITERAL_CODES; ++i) {
out->red_[i] = a->red_[i] + b->red_[i];
out->blue_[i] = a->blue_[i] + b->blue_[i];
out->alpha_[i] = a->alpha_[i] + b->alpha_[i];
}
} else {
for (i = 0; i < literal_size; ++i) {
out->literal_[i] += a->literal_[i];
}
for (i = 0; i < NUM_DISTANCE_CODES; ++i) {
out->distance_[i] += a->distance_[i];
}
for (i = 0; i < NUM_LITERAL_CODES; ++i) {
out->red_[i] += a->red_[i];
out->blue_[i] += a->blue_[i];
out->alpha_[i] += a->alpha_[i];
}
}
}
//------------------------------------------------------------------------------
VP8LProcessBlueAndRedFunc VP8LSubtractGreenFromBlueAndRed;
VP8LTransformColorFunc VP8LTransformColor;
VP8LCollectColorBlueTransformsFunc VP8LCollectColorBlueTransforms;
VP8LCollectColorRedTransformsFunc VP8LCollectColorRedTransforms;
VP8LFastLog2SlowFunc VP8LFastLog2Slow;
VP8LFastLog2SlowFunc VP8LFastSLog2Slow;
VP8LCostFunc VP8LExtraCost;
VP8LCostCombinedFunc VP8LExtraCostCombined;
VP8LCostCountFunc VP8LHuffmanCostCount;
VP8LHistogramAddFunc VP8LHistogramAdd;
extern void VP8LEncDspInitSSE2(void);
extern void VP8LEncDspInitSSE41(void);
extern void VP8LEncDspInitNEON(void);
extern void VP8LEncDspInitMIPS32(void);
extern void VP8LEncDspInitMIPSdspR2(void);
static volatile VP8CPUInfo lossless_enc_last_cpuinfo_used =
(VP8CPUInfo)&lossless_enc_last_cpuinfo_used;
WEBP_TSAN_IGNORE_FUNCTION void VP8LEncDspInit(void) {
if (lossless_enc_last_cpuinfo_used == VP8GetCPUInfo) return;
VP8LDspInit();
VP8LSubtractGreenFromBlueAndRed = VP8LSubtractGreenFromBlueAndRed_C;
VP8LTransformColor = VP8LTransformColor_C;
VP8LCollectColorBlueTransforms = VP8LCollectColorBlueTransforms_C;
VP8LCollectColorRedTransforms = VP8LCollectColorRedTransforms_C;
VP8LFastLog2Slow = FastLog2Slow;
VP8LFastSLog2Slow = FastSLog2Slow;
VP8LExtraCost = ExtraCost;
VP8LExtraCostCombined = ExtraCostCombined;
VP8LHuffmanCostCount = HuffmanCostCount;
VP8LHistogramAdd = HistogramAdd;
// If defined, use CPUInfo() to overwrite some pointers with faster versions.
if (VP8GetCPUInfo != NULL) {
#if defined(WEBP_USE_SSE2)
if (VP8GetCPUInfo(kSSE2)) {
VP8LEncDspInitSSE2();
#if defined(WEBP_USE_SSE41)
if (VP8GetCPUInfo(kSSE4_1)) {
VP8LEncDspInitSSE41();
}
#endif
}
#endif
#if defined(WEBP_USE_NEON)
if (VP8GetCPUInfo(kNEON)) {
VP8LEncDspInitNEON();
}
#endif
#if defined(WEBP_USE_MIPS32)
if (VP8GetCPUInfo(kMIPS32)) {
VP8LEncDspInitMIPS32();
}
#endif
#if defined(WEBP_USE_MIPS_DSP_R2)
if (VP8GetCPUInfo(kMIPSdspR2)) {
VP8LEncDspInitMIPSdspR2();
}
#endif
}
lossless_enc_last_cpuinfo_used = VP8GetCPUInfo;
}
//------------------------------------------------------------------------------