| // 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) |
| |
| static const int kPredLowEffort = 11; |
| static const uint32_t kMaskAlpha = 0xff000000; |
| |
| // 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 |
| }; |
| |
| 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]; |
| if (x != 0) { |
| const int xy = x + Y[i]; |
| sumX += x; |
| retval -= VP8LFastSLog2(x); |
| sumXY += xy; |
| retval -= VP8LFastSLog2(xy); |
| } else if (Y[i] != 0) { |
| sumXY += Y[i]; |
| retval -= VP8LFastSLog2(Y[i]); |
| } |
| } |
| 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 += VP8LCombinedShannonEntropy(tile[i], accumulated[i]); |
| } |
| return (float)retval; |
| } |
| |
| void VP8LBitEntropyInit(VP8LBitEntropy* const entropy) { |
| entropy->entropy = 0.; |
| entropy->sum = 0; |
| entropy->nonzeros = 0; |
| entropy->max_val = 0; |
| entropy->nonzero_code = VP8L_NON_TRIVIAL_SYM; |
| } |
| |
| void VP8LBitsEntropyUnrefined(const uint32_t* const array, int n, |
| VP8LBitEntropy* const entropy) { |
| int i; |
| |
| VP8LBitEntropyInit(entropy); |
| |
| for (i = 0; i < n; ++i) { |
| if (array[i] != 0) { |
| entropy->sum += array[i]; |
| entropy->nonzero_code = i; |
| ++entropy->nonzeros; |
| entropy->entropy -= VP8LFastSLog2(array[i]); |
| if (entropy->max_val < array[i]) { |
| entropy->max_val = array[i]; |
| } |
| } |
| } |
| entropy->entropy += VP8LFastSLog2(entropy->sum); |
| } |
| |
| static WEBP_INLINE void GetEntropyUnrefinedHelper( |
| uint32_t val, int i, uint32_t* const val_prev, int* const i_prev, |
| VP8LBitEntropy* const bit_entropy, VP8LStreaks* const stats) { |
| const int streak = i - *i_prev; |
| |
| // Gather info for the bit entropy. |
| if (*val_prev != 0) { |
| bit_entropy->sum += (*val_prev) * streak; |
| bit_entropy->nonzeros += streak; |
| bit_entropy->nonzero_code = *i_prev; |
| bit_entropy->entropy -= VP8LFastSLog2(*val_prev) * streak; |
| if (bit_entropy->max_val < *val_prev) { |
| bit_entropy->max_val = *val_prev; |
| } |
| } |
| |
| // Gather info for the Huffman cost. |
| stats->counts[*val_prev != 0] += (streak > 3); |
| stats->streaks[*val_prev != 0][(streak > 3)] += streak; |
| |
| *val_prev = val; |
| *i_prev = i; |
| } |
| |
| void VP8LGetEntropyUnrefined(const uint32_t* const X, int length, |
| VP8LBitEntropy* const bit_entropy, |
| VP8LStreaks* const stats) { |
| int i; |
| int i_prev = 0; |
| uint32_t x_prev = X[0]; |
| |
| memset(stats, 0, sizeof(*stats)); |
| VP8LBitEntropyInit(bit_entropy); |
| |
| for (i = 1; i < length; ++i) { |
| const uint32_t x = X[i]; |
| if (x != x_prev) { |
| VP8LGetEntropyUnrefinedHelper(x, i, &x_prev, &i_prev, bit_entropy, stats); |
| } |
| } |
| VP8LGetEntropyUnrefinedHelper(0, i, &x_prev, &i_prev, bit_entropy, stats); |
| |
| bit_entropy->entropy += VP8LFastSLog2(bit_entropy->sum); |
| } |
| |
| void VP8LGetCombinedEntropyUnrefined(const uint32_t* const X, |
| const uint32_t* const Y, int length, |
| VP8LBitEntropy* const bit_entropy, |
| VP8LStreaks* const stats) { |
| int i = 1; |
| int i_prev = 0; |
| uint32_t xy_prev = X[0] + Y[0]; |
| |
| memset(stats, 0, sizeof(*stats)); |
| VP8LBitEntropyInit(bit_entropy); |
| |
| for (i = 1; i < length; ++i) { |
| const uint32_t xy = X[i] + Y[i]; |
| if (xy != xy_prev) { |
| VP8LGetEntropyUnrefinedHelper(xy, i, &xy_prev, &i_prev, bit_entropy, |
| stats); |
| } |
| } |
| VP8LGetEntropyUnrefinedHelper(0, i, &xy_prev, &i_prev, bit_entropy, stats); |
| |
| bit_entropy->entropy += VP8LFastSLog2(bit_entropy->sum); |
| } |
| |
| 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]; |
| } |
| |
| //------------------------------------------------------------------------------ |
| |
| static WEBP_INLINE uint32_t Predict(VP8LPredictorFunc pred_func, |
| int x, int y, |
| const uint32_t* current_row, |
| const uint32_t* upper_row) { |
| if (y == 0) { |
| return (x == 0) ? ARGB_BLACK : current_row[x - 1]; // Left. |
| } else if (x == 0) { |
| return upper_row[x]; // Top. |
| } else { |
| return pred_func(current_row[x - 1], upper_row + x); |
| } |
| } |
| |
| // 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, |
| int exact) { |
| 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; |
| const uint32_t predict = |
| Predict(pred_func, col, row, current_row, upper_row); |
| uint32_t residual = VP8LSubPixels(current_row[col], predict); |
| if (!exact && (current_row[col] & kMaskAlpha) == 0) { |
| residual &= kMaskAlpha; // See CopyTileWithPrediction. |
| } |
| UpdateHisto(histo_argb, residual); |
| } |
| } |
| 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, |
| int low_effort, int exact) { |
| 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 = |
| low_effort ? VP8LPredictors[kPredLowEffort] : NULL; |
| |
| 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; |
| |
| if (low_effort) { |
| for (x = 0; x < width; ++x) { |
| const uint32_t predict = |
| Predict(pred_func, x, y, current_row, upper_row); |
| argb[y * width + x] = VP8LSubPixels(current_row[x], predict); |
| } |
| } else { |
| for (x = 0; x < width; ++x) { |
| uint32_t predict, residual; |
| if ((x & mask) == 0) { |
| const int mode = |
| (modes[(y >> bits) * tiles_per_row + (x >> bits)] >> 8) & 0xff; |
| pred_func = VP8LPredictors[mode]; |
| } |
| predict = Predict(pred_func, x, y, current_row, upper_row); |
| residual = VP8LSubPixels(current_row[x], predict); |
| if (!exact && (current_row[x] & kMaskAlpha) == 0) { |
| // If alpha is 0, cleanup RGB. We can choose the RGB values of the |
| // residual for best compression. The prediction of alpha itself can |
| // be non-zero and must be kept though. We choose RGB of the residual |
| // to be 0. |
| residual &= kMaskAlpha; |
| // Update input image so that next predictions use correct RGB value. |
| current_row[x] = predict & ~kMaskAlpha; |
| if (x == 0 && y != 0) upper_row[width] = current_row[x]; |
| } |
| argb[y * width + x] = residual; |
| } |
| } |
| } |
| } |
| |
| void VP8LResidualImage(int width, int height, int bits, int low_effort, |
| uint32_t* const argb, uint32_t* const argb_scratch, |
| uint32_t* const image, int exact) { |
| const int max_tile_size = 1 << bits; |
| const int tiles_per_row = VP8LSubSampleSize(width, bits); |
| const int tiles_per_col = VP8LSubSampleSize(height, bits); |
| 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) { |
| int i; |
| for (i = 0; i < tiles_per_row * tiles_per_col; ++i) { |
| image[i] = ARGB_BLACK | (kPredLowEffort << 8); |
| } |
| } else { |
| 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 = GetBestPredictorForTile(width, height, tile_x, tile_y, |
| bits, (int (*)[256])histo, argb_scratch, exact); |
| image[tile_y * tiles_per_row + tile_x] = ARGB_BLACK | (pred << 8); |
| } |
| } |
| } |
| |
| CopyImageWithPrediction(width, height, bits, |
| image, argb_scratch, argb, low_effort, exact); |
| } |
| |
| 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 VP8LCombinedShannonEntropy(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; |
| } |
| |
| //------------------------------------------------------------------------------ |
| |
| 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; |
| VP8LCombinedShannonEntropyFunc VP8LCombinedShannonEntropy; |
| |
| GetEntropyUnrefinedHelperFunc VP8LGetEntropyUnrefinedHelper; |
| |
| 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; |
| VP8LCombinedShannonEntropy = CombinedShannonEntropy; |
| |
| VP8LGetEntropyUnrefinedHelper = GetEntropyUnrefinedHelper; |
| |
| 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; |
| } |
| |
| //------------------------------------------------------------------------------ |
