diff options
Diffstat (limited to 'drivers/webp/dsp/lossless_enc.c')
| -rw-r--r-- | drivers/webp/dsp/lossless_enc.c | 1305 |
1 files changed, 1305 insertions, 0 deletions
diff --git a/drivers/webp/dsp/lossless_enc.c b/drivers/webp/dsp/lossless_enc.c new file mode 100644 index 0000000000..b3036f5384 --- /dev/null +++ b/drivers/webp/dsp/lossless_enc.c @@ -0,0 +1,1305 @@ +// 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; +} + +//------------------------------------------------------------------------------ |