mirror of
https://github.com/webmproject/libwebp.git
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remove use of log2(). Use VP8LFastLog2() instead.
Order-by-cost mostly unchanged (up to a scaling constant 1/log(2)) (except for few minor diff in < 2% of cases) + remove unused field cost_mode->cache_bits_ Change-Id: I714f8ab12f49a23f5d499a64c741382c9b489a3e
This commit is contained in:
Pascal Massimino
@ -23,113 +23,156 @@ extern "C" {
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#include "../dsp/dsp.h"
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#include "../enc/histogram.h"
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// A lookup table for small values of log(int) to be used in entropy
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// computation.
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//
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// ", ".join(["%.16ff" % x for x in [0.0]+[log(x) for x in range(1, 256)]])
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#define MAX_DIFF_COST (1e30f)
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// lookup table for small values of log2(int)
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#define APPROX_LOG_MAX 4096
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#define LOG_2_RECIPROCAL 1.44269504088896338700465094007086
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#define LOG_LOOKUP_IDX_MAX 256
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static const float kLogTable[LOG_LOOKUP_IDX_MAX] = {
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0.0000000000000000f, 0.0000000000000000f, 0.6931471805599453f,
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1.0986122886681098f, 1.3862943611198906f, 1.6094379124341003f,
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1.7917594692280550f, 1.9459101490553132f, 2.0794415416798357f,
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2.1972245773362196f, 2.3025850929940459f, 2.3978952727983707f,
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2.4849066497880004f, 2.5649493574615367f, 2.6390573296152584f,
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2.7080502011022101f, 2.7725887222397811f, 2.8332133440562162f,
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2.8903717578961645f, 2.9444389791664403f, 2.9957322735539909f,
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3.0445224377234230f, 3.0910424533583161f, 3.1354942159291497f,
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3.1780538303479458f, 3.2188758248682006f, 3.2580965380214821f,
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3.2958368660043291f, 3.3322045101752038f, 3.3672958299864741f,
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3.4011973816621555f, 3.4339872044851463f, 3.4657359027997265f,
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3.4965075614664802f, 3.5263605246161616f, 3.5553480614894135f,
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3.5835189384561099f, 3.6109179126442243f, 3.6375861597263857f,
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3.6635616461296463f, 3.6888794541139363f, 3.7135720667043080f,
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3.7376696182833684f, 3.7612001156935624f, 3.7841896339182610f,
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3.8066624897703196f, 3.8286413964890951f, 3.8501476017100584f,
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3.8712010109078911f, 3.8918202981106265f, 3.9120230054281460f,
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3.9318256327243257f, 3.9512437185814275f, 3.9702919135521220f,
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3.9889840465642745f, 4.0073331852324712f, 4.0253516907351496f,
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4.0430512678345503f, 4.0604430105464191f, 4.0775374439057197f,
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4.0943445622221004f, 4.1108738641733114f, 4.1271343850450917f,
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4.1431347263915326f, 4.1588830833596715f, 4.1743872698956368f,
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4.1896547420264252f, 4.2046926193909657f, 4.2195077051761070f,
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4.2341065045972597f, 4.2484952420493594f, 4.2626798770413155f,
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4.2766661190160553f, 4.2904594411483910f, 4.3040650932041702f,
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4.3174881135363101f, 4.3307333402863311f, 4.3438054218536841f,
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4.3567088266895917f, 4.3694478524670215f, 4.3820266346738812f,
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4.3944491546724391f, 4.4067192472642533f, 4.4188406077965983f,
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4.4308167988433134f, 4.4426512564903167f, 4.4543472962535073f,
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4.4659081186545837f, 4.4773368144782069f, 4.4886363697321396f,
|
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4.4998096703302650f, 4.5108595065168497f, 4.5217885770490405f,
|
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4.5325994931532563f, 4.5432947822700038f, 4.5538768916005408f,
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4.5643481914678361f, 4.5747109785033828f, 4.5849674786705723f,
|
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4.5951198501345898f, 4.6051701859880918f, 4.6151205168412597f,
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4.6249728132842707f, 4.6347289882296359f, 4.6443908991413725f,
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4.6539603501575231f, 4.6634390941120669f, 4.6728288344619058f,
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4.6821312271242199f, 4.6913478822291435f, 4.7004803657924166f,
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4.7095302013123339f, 4.7184988712950942f, 4.7273878187123408f,
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4.7361984483944957f, 4.7449321283632502f, 4.7535901911063645f,
|
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4.7621739347977563f, 4.7706846244656651f, 4.7791234931115296f,
|
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4.7874917427820458f, 4.7957905455967413f, 4.8040210447332568f,
|
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4.8121843553724171f, 4.8202815656050371f, 4.8283137373023015f,
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4.8362819069514780f, 4.8441870864585912f, 4.8520302639196169f,
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4.8598124043616719f, 4.8675344504555822f, 4.8751973232011512f,
|
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4.8828019225863706f, 4.8903491282217537f, 4.8978397999509111f,
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4.9052747784384296f, 4.9126548857360524f, 4.9199809258281251f,
|
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4.9272536851572051f, 4.9344739331306915f, 4.9416424226093039f,
|
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4.9487598903781684f, 4.9558270576012609f, 4.9628446302599070f,
|
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4.9698132995760007f, 4.9767337424205742f, 4.9836066217083363f,
|
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4.9904325867787360f, 4.9972122737641147f, 5.0039463059454592f,
|
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5.0106352940962555f, 5.0172798368149243f, 5.0238805208462765f,
|
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5.0304379213924353f, 5.0369526024136295f, 5.0434251169192468f,
|
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5.0498560072495371f, 5.0562458053483077f, 5.0625950330269669f,
|
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5.0689042022202315f, 5.0751738152338266f, 5.0814043649844631f,
|
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5.0875963352323836f, 5.0937502008067623f, 5.0998664278241987f,
|
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5.1059454739005803f, 5.1119877883565437f, 5.1179938124167554f,
|
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5.1239639794032588f, 5.1298987149230735f, 5.1357984370502621f,
|
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5.1416635565026603f, 5.1474944768134527f, 5.1532915944977793f,
|
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5.1590552992145291f, 5.1647859739235145f, 5.1704839950381514f,
|
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5.1761497325738288f, 5.1817835502920850f, 5.1873858058407549f,
|
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5.1929568508902104f, 5.1984970312658261f, 5.2040066870767951f,
|
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5.2094861528414214f, 5.2149357576089859f, 5.2203558250783244f,
|
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5.2257466737132017f, 5.2311086168545868f, 5.2364419628299492f,
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5.2417470150596426f, 5.2470240721604862f, 5.2522734280466299f,
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5.2574953720277815f, 5.2626901889048856f, 5.2678581590633282f,
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5.2729995585637468f, 5.2781146592305168f, 5.2832037287379885f,
|
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5.2882670306945352f, 5.2933048247244923f, 5.2983173665480363f,
|
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5.3033049080590757f, 5.3082676974012051f, 5.3132059790417872f,
|
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5.3181199938442161f, 5.3230099791384085f, 5.3278761687895813f,
|
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5.3327187932653688f, 5.3375380797013179f, 5.3423342519648109f,
|
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5.3471075307174685f, 5.3518581334760666f, 5.3565862746720123f,
|
||||
5.3612921657094255f, 5.3659760150218512f, 5.3706380281276624f,
|
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5.3752784076841653f, 5.3798973535404597f, 5.3844950627890888f,
|
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5.3890717298165010f, 5.3936275463523620f, 5.3981627015177525f,
|
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5.4026773818722793f, 5.4071717714601188f, 5.4116460518550396f,
|
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5.4161004022044201f, 5.4205349992722862f, 5.4249500174814029f,
|
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5.4293456289544411f, 5.4337220035542400f, 5.4380793089231956f,
|
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5.4424177105217932f, 5.4467373716663099f, 5.4510384535657002f,
|
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5.4553211153577017f, 5.4595855141441589f, 5.4638318050256105f,
|
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5.4680601411351315f, 5.4722706736714750f, 5.4764635519315110f,
|
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5.4806389233419912f, 5.4847969334906548f, 5.4889377261566867f,
|
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5.4930614433405482f, 5.4971682252932021f, 5.5012582105447274f,
|
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5.5053315359323625f, 5.5093883366279774f, 5.5134287461649825f,
|
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5.5174528964647074f, 5.5214609178622460f, 5.5254529391317835f,
|
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5.5294290875114234f, 5.5333894887275203f, 5.5373342670185366f,
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5.5412635451584258f
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static const float kLog2Table[LOG_LOOKUP_IDX_MAX] = {
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0.0000000000000000f, 0.0000000000000000f,
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1.0000000000000000f, 1.5849625007211560f,
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2.0000000000000000f, 2.3219280948873621f,
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2.5849625007211560f, 2.8073549220576041f,
|
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3.0000000000000000f, 3.1699250014423121f,
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3.3219280948873621f, 3.4594316186372973f,
|
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3.5849625007211560f, 3.7004397181410921f,
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3.8073549220576041f, 3.9068905956085187f,
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4.0000000000000000f, 4.0874628412503390f,
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4.1699250014423121f, 4.2479275134435852f,
|
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4.3219280948873626f, 4.3923174227787606f,
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4.4594316186372973f, 4.5235619560570130f,
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4.5849625007211560f, 4.6438561897747243f,
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4.7004397181410917f, 4.7548875021634682f,
|
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4.8073549220576037f, 4.8579809951275718f,
|
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4.9068905956085187f, 4.9541963103868749f,
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5.0000000000000000f, 5.0443941193584533f,
|
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5.0874628412503390f, 5.1292830169449663f,
|
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5.1699250014423121f, 5.2094533656289501f,
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5.2479275134435852f, 5.2854022188622487f,
|
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5.3219280948873626f, 5.3575520046180837f,
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5.3923174227787606f, 5.4262647547020979f,
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5.4594316186372973f, 5.4918530963296747f,
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5.5235619560570130f, 5.5545888516776376f,
|
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5.5849625007211560f, 5.6147098441152083f,
|
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5.6438561897747243f, 5.6724253419714951f,
|
||||
5.7004397181410917f, 5.7279204545631987f,
|
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5.7548875021634682f, 5.7813597135246599f,
|
||||
5.8073549220576037f, 5.8328900141647412f,
|
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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
|
||||
};
|
||||
|
||||
#define APPROX_LOG_MAX 4096
|
||||
#define LOG_2_BASE_E 0.6931471805599453f
|
||||
|
||||
float VP8LFastLog(int v) {
|
||||
if (v < APPROX_LOG_MAX) {
|
||||
float VP8LFastLog2(int v) {
|
||||
if (v < LOG_LOOKUP_IDX_MAX) {
|
||||
return kLog2Table[v];
|
||||
} else if (v < APPROX_LOG_MAX) {
|
||||
int log_cnt = 0;
|
||||
while (v >= LOG_LOOKUP_IDX_MAX) {
|
||||
++log_cnt;
|
||||
v = v >> 1;
|
||||
}
|
||||
return kLogTable[v] + (log_cnt * LOG_2_BASE_E);
|
||||
return kLog2Table[v] + (float)log_cnt;
|
||||
} else {
|
||||
return (float)(LOG_2_RECIPROCAL * log((double)v));
|
||||
}
|
||||
return (float)log(v);
|
||||
}
|
||||
|
||||
//------------------------------------------------------------------------------
|
||||
@ -284,7 +327,7 @@ static const PredictorFunc kPredictors[16] = {
|
||||
};
|
||||
|
||||
// TODO(vikasa): Replace 256 etc with defines.
|
||||
static double PredictionCostSpatial(const int* counts,
|
||||
static float PredictionCostSpatial(const int* counts,
|
||||
int weight_0, double exp_val) {
|
||||
const int significant_symbols = 16;
|
||||
const double exp_decay_factor = 0.6;
|
||||
@ -294,27 +337,26 @@ static double PredictionCostSpatial(const int* counts,
|
||||
bits += exp_val * (counts[i] + counts[256 - i]);
|
||||
exp_val *= exp_decay_factor;
|
||||
}
|
||||
return -0.1 * bits;
|
||||
return (float)(-0.1 * bits);
|
||||
}
|
||||
|
||||
// Compute the Shanon's entropy: Sum(p*log2(p))
|
||||
static double ShannonEntropy(const int* const array, int n) {
|
||||
static float ShannonEntropy(const int* const array, int n) {
|
||||
int i;
|
||||
double retval = 0;
|
||||
float retval = 0.f;
|
||||
int sum = 0;
|
||||
for (i = 0; i < n; ++i) {
|
||||
if (array[i] != 0) {
|
||||
sum += array[i];
|
||||
retval += array[i] * VP8LFastLog(array[i]);
|
||||
retval -= VP8LFastSLog2(array[i]);
|
||||
}
|
||||
}
|
||||
retval -= sum * VP8LFastLog(sum);
|
||||
retval *= -1.4426950408889634; // 1.0 / -FastLog(2);
|
||||
retval += VP8LFastSLog2(sum);
|
||||
return retval;
|
||||
}
|
||||
|
||||
static double PredictionCostSpatialHistogram(int accumulated[4][256],
|
||||
int tile[4][256]) {
|
||||
static float PredictionCostSpatialHistogram(int accumulated[4][256],
|
||||
int tile[4][256]) {
|
||||
int i;
|
||||
int k;
|
||||
int combo[256];
|
||||
@ -328,7 +370,7 @@ static double PredictionCostSpatialHistogram(int accumulated[4][256],
|
||||
}
|
||||
retval += ShannonEntropy(&combo[0], 256);
|
||||
}
|
||||
return retval;
|
||||
return (float)retval;
|
||||
}
|
||||
|
||||
static int GetBestPredictorForTile(int width, int height,
|
||||
@ -344,14 +386,14 @@ static int GetBestPredictorForTile(int width, int height,
|
||||
const int xmax = (tile_size <= width - col_start) ?
|
||||
tile_size : width - col_start;
|
||||
int histo[4][256];
|
||||
double best_diff = 1e99;
|
||||
float best_diff = MAX_DIFF_COST;
|
||||
int best_mode = 0;
|
||||
|
||||
int mode;
|
||||
for (mode = 0; mode < kNumPredModes; ++mode) {
|
||||
const uint32_t* current_row = argb_scratch;
|
||||
const PredictorFunc pred_func = kPredictors[mode];
|
||||
double cur_diff;
|
||||
float cur_diff;
|
||||
int y;
|
||||
memset(&histo[0][0], 0, sizeof(histo));
|
||||
for (y = 0; y < ymax; ++y) {
|
||||
@ -630,8 +672,8 @@ static WEBP_INLINE int SkipRepeatedPixels(const uint32_t* const argb,
|
||||
return 0;
|
||||
}
|
||||
|
||||
static double PredictionCostCrossColor(const int accumulated[256],
|
||||
const int counts[256]) {
|
||||
static float PredictionCostCrossColor(const int accumulated[256],
|
||||
const int counts[256]) {
|
||||
// Favor low entropy, locally and globally.
|
||||
int i;
|
||||
int combo[256];
|
||||
@ -651,8 +693,8 @@ static Multipliers GetBestColorTransformForTile(
|
||||
int* accumulated_red_histo,
|
||||
int* accumulated_blue_histo,
|
||||
const uint32_t* const argb) {
|
||||
double best_diff = 1e99;
|
||||
double cur_diff;
|
||||
float best_diff = MAX_DIFF_COST;
|
||||
float cur_diff;
|
||||
const int halfstep = step / 2;
|
||||
const int max_tile_size = 1 << bits;
|
||||
const int tile_y_offset = tile_y * max_tile_size;
|
||||
@ -704,7 +746,7 @@ static Multipliers GetBestColorTransformForTile(
|
||||
best_tx = tx;
|
||||
}
|
||||
}
|
||||
best_diff = 1e99;
|
||||
best_diff = MAX_DIFF_COST;
|
||||
green_to_red = best_tx.green_to_red_;
|
||||
for (green_to_blue = -32; green_to_blue <= 32; green_to_blue += step) {
|
||||
for (red_to_blue = -32; red_to_blue <= 32; red_to_blue += step) {
|
||||
|
||||
@ -59,8 +59,10 @@ static WEBP_INLINE uint32_t VP8LSubSampleSize(uint32_t size,
|
||||
return (size + (1 << sampling_bits) - 1) >> sampling_bits;
|
||||
}
|
||||
|
||||
// Faster logarithm for small integers, with the property of log(0) == 0.
|
||||
float VP8LFastLog(int v);
|
||||
// Faster logarithm for integers, with the property of log2(0) == 0.
|
||||
float VP8LFastLog2(int v);
|
||||
// Fast calculation of v * log2(v) for integer input.
|
||||
static WEBP_INLINE float VP8LFastSLog2(int v) { return VP8LFastLog2(v) * v; }
|
||||
|
||||
// In-place difference of each component with mod 256.
|
||||
static WEBP_INLINE uint32_t VP8LSubPixels(uint32_t a, uint32_t b) {
|
||||
|
||||
Reference in New Issue
Block a user