7131cb0e3d
Removing unused constants, macros, and function declarations. Using ROUND_POWER_OF_TWO macro, vp9_zero, vp9_copy where possible. Moving #include from *.h to *.c. Merging for loops for motion vectors. Change-Id: Ic3bf841764a2bb177128bb3a6d7aa8f68229cd13
662 lines
32 KiB
C
662 lines
32 KiB
C
/*
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* Copyright (c) 2010 The WebM project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "vp9/common/vp9_entropy.h"
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#include "vp9/common/vp9_blockd.h"
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#include "vp9/common/vp9_onyxc_int.h"
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#include "vp9/common/vp9_entropymode.h"
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#include "vpx_mem/vpx_mem.h"
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#include "vpx/vpx_integer.h"
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#define MODEL_NODES (ENTROPY_NODES - UNCONSTRAINED_NODES)
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DECLARE_ALIGNED(16, const uint8_t, vp9_norm[256]) = {
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0, 7, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4,
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3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
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};
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DECLARE_ALIGNED(16, const uint8_t,
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vp9_coefband_trans_8x8plus[MAXBAND_INDEX + 1]) = {
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0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,
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4, 4, 4, 4, 4, 5
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};
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DECLARE_ALIGNED(16, const uint8_t,
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vp9_coefband_trans_4x4[MAXBAND_INDEX + 1]) = {
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0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5,
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5, 5, 5, 5, 5, 5
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};
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DECLARE_ALIGNED(16, const uint8_t, vp9_pt_energy_class[MAX_ENTROPY_TOKENS]) = {
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0, 1, 2, 3, 3, 4, 4, 5, 5, 5, 5, 5
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_default_scan_4x4[16]) = {
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0, 4, 1, 5,
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8, 2, 12, 9,
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3, 6, 13, 10,
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7, 14, 11, 15,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_col_scan_4x4[16]) = {
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0, 4, 8, 1,
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12, 5, 9, 2,
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13, 6, 10, 3,
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7, 14, 11, 15,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_row_scan_4x4[16]) = {
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0, 1, 4, 2,
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5, 3, 6, 8,
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9, 7, 12, 10,
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13, 11, 14, 15,
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};
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DECLARE_ALIGNED(64, const int16_t, vp9_default_scan_8x8[64]) = {
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0, 8, 1, 16, 9, 2, 17, 24,
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10, 3, 18, 25, 32, 11, 4, 26,
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33, 19, 40, 12, 34, 27, 5, 41,
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20, 48, 13, 35, 42, 28, 21, 6,
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49, 56, 36, 43, 29, 7, 14, 50,
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57, 44, 22, 37, 15, 51, 58, 30,
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45, 23, 52, 59, 38, 31, 60, 53,
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46, 39, 61, 54, 47, 62, 55, 63,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_col_scan_8x8[64]) = {
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0, 8, 16, 1, 24, 9, 32, 17,
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2, 40, 25, 10, 33, 18, 48, 3,
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26, 41, 11, 56, 19, 34, 4, 49,
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27, 42, 12, 35, 20, 57, 50, 28,
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5, 43, 13, 36, 58, 51, 21, 44,
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6, 29, 59, 37, 14, 52, 22, 7,
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45, 60, 30, 15, 38, 53, 23, 46,
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31, 61, 39, 54, 47, 62, 55, 63,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_row_scan_8x8[64]) = {
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0, 1, 2, 8, 9, 3, 16, 10,
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4, 17, 11, 24, 5, 18, 25, 12,
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19, 26, 32, 6, 13, 20, 33, 27,
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7, 34, 40, 21, 28, 41, 14, 35,
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48, 42, 29, 36, 49, 22, 43, 15,
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56, 37, 50, 44, 30, 57, 23, 51,
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58, 45, 38, 52, 31, 59, 53, 46,
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60, 39, 61, 47, 54, 55, 62, 63,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_default_scan_16x16[256]) = {
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0, 16, 1, 32, 17, 2, 48, 33, 18, 3, 64, 34, 49, 19, 65, 80,
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50, 4, 35, 66, 20, 81, 96, 51, 5, 36, 82, 97, 67, 112, 21, 52,
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98, 37, 83, 113, 6, 68, 128, 53, 22, 99, 114, 84, 7, 129, 38, 69,
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100, 115, 144, 130, 85, 54, 23, 8, 145, 39, 70, 116, 101, 131, 160, 146,
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55, 86, 24, 71, 132, 117, 161, 40, 9, 102, 147, 176, 162, 87, 56, 25,
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133, 118, 177, 148, 72, 103, 41, 163, 10, 192, 178, 88, 57, 134, 149, 119,
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26, 164, 73, 104, 193, 42, 179, 208, 11, 135, 89, 165, 120, 150, 58, 194,
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180, 27, 74, 209, 105, 151, 136, 43, 90, 224, 166, 195, 181, 121, 210, 59,
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12, 152, 106, 167, 196, 75, 137, 225, 211, 240, 182, 122, 91, 28, 197, 13,
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226, 168, 183, 153, 44, 212, 138, 107, 241, 60, 29, 123, 198, 184, 227, 169,
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242, 76, 213, 154, 45, 92, 14, 199, 139, 61, 228, 214, 170, 185, 243, 108,
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77, 155, 30, 15, 200, 229, 124, 215, 244, 93, 46, 186, 171, 201, 109, 140,
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230, 62, 216, 245, 31, 125, 78, 156, 231, 47, 187, 202, 217, 94, 246, 141,
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63, 232, 172, 110, 247, 157, 79, 218, 203, 126, 233, 188, 248, 95, 173, 142,
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219, 111, 249, 234, 158, 127, 189, 204, 250, 235, 143, 174, 220, 205, 159, 251,
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190, 221, 175, 236, 237, 191, 206, 252, 222, 253, 207, 238, 223, 254, 239, 255,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_col_scan_16x16[256]) = {
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0, 16, 32, 48, 1, 64, 17, 80, 33, 96, 49, 2, 65, 112, 18, 81,
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34, 128, 50, 97, 3, 66, 144, 19, 113, 35, 82, 160, 98, 51, 129, 4,
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67, 176, 20, 114, 145, 83, 36, 99, 130, 52, 192, 5, 161, 68, 115, 21,
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146, 84, 208, 177, 37, 131, 100, 53, 162, 224, 69, 6, 116, 193, 147, 85,
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22, 240, 132, 38, 178, 101, 163, 54, 209, 117, 70, 7, 148, 194, 86, 179,
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225, 23, 133, 39, 164, 8, 102, 210, 241, 55, 195, 118, 149, 71, 180, 24,
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87, 226, 134, 165, 211, 40, 103, 56, 72, 150, 196, 242, 119, 9, 181, 227,
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88, 166, 25, 135, 41, 104, 212, 57, 151, 197, 120, 73, 243, 182, 136, 167,
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213, 89, 10, 228, 105, 152, 198, 26, 42, 121, 183, 244, 168, 58, 137, 229,
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74, 214, 90, 153, 199, 184, 11, 106, 245, 27, 122, 230, 169, 43, 215, 59,
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200, 138, 185, 246, 75, 12, 91, 154, 216, 231, 107, 28, 44, 201, 123, 170,
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60, 247, 232, 76, 139, 13, 92, 217, 186, 248, 155, 108, 29, 124, 45, 202,
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233, 171, 61, 14, 77, 140, 15, 249, 93, 30, 187, 156, 218, 46, 109, 125,
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62, 172, 78, 203, 31, 141, 234, 94, 47, 188, 63, 157, 110, 250, 219, 79,
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126, 204, 173, 142, 95, 189, 111, 235, 158, 220, 251, 127, 174, 143, 205, 236,
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159, 190, 221, 252, 175, 206, 237, 191, 253, 222, 238, 207, 254, 223, 239, 255,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_row_scan_16x16[256]) = {
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0, 1, 2, 16, 3, 17, 4, 18, 32, 5, 33, 19, 6, 34, 48, 20,
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49, 7, 35, 21, 50, 64, 8, 36, 65, 22, 51, 37, 80, 9, 66, 52,
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23, 38, 81, 67, 10, 53, 24, 82, 68, 96, 39, 11, 54, 83, 97, 69,
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25, 98, 84, 40, 112, 55, 12, 70, 99, 113, 85, 26, 41, 56, 114, 100,
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13, 71, 128, 86, 27, 115, 101, 129, 42, 57, 72, 116, 14, 87, 130, 102,
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144, 73, 131, 117, 28, 58, 15, 88, 43, 145, 103, 132, 146, 118, 74, 160,
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89, 133, 104, 29, 59, 147, 119, 44, 161, 148, 90, 105, 134, 162, 120, 176,
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75, 135, 149, 30, 60, 163, 177, 45, 121, 91, 106, 164, 178, 150, 192, 136,
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165, 179, 31, 151, 193, 76, 122, 61, 137, 194, 107, 152, 180, 208, 46, 166,
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167, 195, 92, 181, 138, 209, 123, 153, 224, 196, 77, 168, 210, 182, 240, 108,
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197, 62, 154, 225, 183, 169, 211, 47, 139, 93, 184, 226, 212, 241, 198, 170,
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124, 155, 199, 78, 213, 185, 109, 227, 200, 63, 228, 242, 140, 214, 171, 186,
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156, 229, 243, 125, 94, 201, 244, 215, 216, 230, 141, 187, 202, 79, 172, 110,
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157, 245, 217, 231, 95, 246, 232, 126, 203, 247, 233, 173, 218, 142, 111, 158,
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188, 248, 127, 234, 219, 249, 189, 204, 143, 174, 159, 250, 235, 205, 220, 175,
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190, 251, 221, 191, 206, 236, 207, 237, 252, 222, 253, 223, 238, 239, 254, 255,
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};
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DECLARE_ALIGNED(16, const int16_t, vp9_default_scan_32x32[1024]) = {
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0, 32, 1, 64, 33, 2, 96, 65, 34, 128, 3, 97, 66, 160, 129, 35, 98, 4, 67, 130, 161, 192, 36, 99, 224, 5, 162, 193, 68, 131, 37, 100,
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225, 194, 256, 163, 69, 132, 6, 226, 257, 288, 195, 101, 164, 38, 258, 7, 227, 289, 133, 320, 70, 196, 165, 290, 259, 228, 39, 321, 102, 352, 8, 197,
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71, 134, 322, 291, 260, 353, 384, 229, 166, 103, 40, 354, 323, 292, 135, 385, 198, 261, 72, 9, 416, 167, 386, 355, 230, 324, 104, 293, 41, 417, 199, 136,
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262, 387, 448, 325, 356, 10, 73, 418, 231, 168, 449, 294, 388, 105, 419, 263, 42, 200, 357, 450, 137, 480, 74, 326, 232, 11, 389, 169, 295, 420, 106, 451,
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481, 358, 264, 327, 201, 43, 138, 512, 482, 390, 296, 233, 170, 421, 75, 452, 359, 12, 513, 265, 483, 328, 107, 202, 514, 544, 422, 391, 453, 139, 44, 234,
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484, 297, 360, 171, 76, 515, 545, 266, 329, 454, 13, 423, 203, 108, 546, 485, 576, 298, 235, 140, 361, 330, 172, 547, 45, 455, 267, 577, 486, 77, 204, 362,
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608, 14, 299, 578, 109, 236, 487, 609, 331, 141, 579, 46, 15, 173, 610, 363, 78, 205, 16, 110, 237, 611, 142, 47, 174, 79, 206, 17, 111, 238, 48, 143,
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80, 175, 112, 207, 49, 18, 239, 81, 113, 19, 50, 82, 114, 51, 83, 115, 640, 516, 392, 268, 144, 20, 672, 641, 548, 517, 424, 393, 300, 269, 176, 145,
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52, 21, 704, 673, 642, 580, 549, 518, 456, 425, 394, 332, 301, 270, 208, 177, 146, 84, 53, 22, 736, 705, 674, 643, 612, 581, 550, 519, 488, 457, 426, 395,
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364, 333, 302, 271, 240, 209, 178, 147, 116, 85, 54, 23, 737, 706, 675, 613, 582, 551, 489, 458, 427, 365, 334, 303, 241, 210, 179, 117, 86, 55, 738, 707,
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614, 583, 490, 459, 366, 335, 242, 211, 118, 87, 739, 615, 491, 367, 243, 119, 768, 644, 520, 396, 272, 148, 24, 800, 769, 676, 645, 552, 521, 428, 397, 304,
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273, 180, 149, 56, 25, 832, 801, 770, 708, 677, 646, 584, 553, 522, 460, 429, 398, 336, 305, 274, 212, 181, 150, 88, 57, 26, 864, 833, 802, 771, 740, 709,
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678, 647, 616, 585, 554, 523, 492, 461, 430, 399, 368, 337, 306, 275, 244, 213, 182, 151, 120, 89, 58, 27, 865, 834, 803, 741, 710, 679, 617, 586, 555, 493,
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462, 431, 369, 338, 307, 245, 214, 183, 121, 90, 59, 866, 835, 742, 711, 618, 587, 494, 463, 370, 339, 246, 215, 122, 91, 867, 743, 619, 495, 371, 247, 123,
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896, 772, 648, 524, 400, 276, 152, 28, 928, 897, 804, 773, 680, 649, 556, 525, 432, 401, 308, 277, 184, 153, 60, 29, 960, 929, 898, 836, 805, 774, 712, 681,
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650, 588, 557, 526, 464, 433, 402, 340, 309, 278, 216, 185, 154, 92, 61, 30, 992, 961, 930, 899, 868, 837, 806, 775, 744, 713, 682, 651, 620, 589, 558, 527,
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496, 465, 434, 403, 372, 341, 310, 279, 248, 217, 186, 155, 124, 93, 62, 31, 993, 962, 931, 869, 838, 807, 745, 714, 683, 621, 590, 559, 497, 466, 435, 373,
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342, 311, 249, 218, 187, 125, 94, 63, 994, 963, 870, 839, 746, 715, 622, 591, 498, 467, 374, 343, 250, 219, 126, 95, 995, 871, 747, 623, 499, 375, 251, 127,
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900, 776, 652, 528, 404, 280, 156, 932, 901, 808, 777, 684, 653, 560, 529, 436, 405, 312, 281, 188, 157, 964, 933, 902, 840, 809, 778, 716, 685, 654, 592, 561,
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530, 468, 437, 406, 344, 313, 282, 220, 189, 158, 996, 965, 934, 903, 872, 841, 810, 779, 748, 717, 686, 655, 624, 593, 562, 531, 500, 469, 438, 407, 376, 345,
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314, 283, 252, 221, 190, 159, 997, 966, 935, 873, 842, 811, 749, 718, 687, 625, 594, 563, 501, 470, 439, 377, 346, 315, 253, 222, 191, 998, 967, 874, 843, 750,
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719, 626, 595, 502, 471, 378, 347, 254, 223, 999, 875, 751, 627, 503, 379, 255, 904, 780, 656, 532, 408, 284, 936, 905, 812, 781, 688, 657, 564, 533, 440, 409,
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316, 285, 968, 937, 906, 844, 813, 782, 720, 689, 658, 596, 565, 534, 472, 441, 410, 348, 317, 286, 1000, 969, 938, 907, 876, 845, 814, 783, 752, 721, 690, 659,
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628, 597, 566, 535, 504, 473, 442, 411, 380, 349, 318, 287, 1001, 970, 939, 877, 846, 815, 753, 722, 691, 629, 598, 567, 505, 474, 443, 381, 350, 319, 1002, 971,
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878, 847, 754, 723, 630, 599, 506, 475, 382, 351, 1003, 879, 755, 631, 507, 383, 908, 784, 660, 536, 412, 940, 909, 816, 785, 692, 661, 568, 537, 444, 413, 972,
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941, 910, 848, 817, 786, 724, 693, 662, 600, 569, 538, 476, 445, 414, 1004, 973, 942, 911, 880, 849, 818, 787, 756, 725, 694, 663, 632, 601, 570, 539, 508, 477,
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446, 415, 1005, 974, 943, 881, 850, 819, 757, 726, 695, 633, 602, 571, 509, 478, 447, 1006, 975, 882, 851, 758, 727, 634, 603, 510, 479, 1007, 883, 759, 635, 511,
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912, 788, 664, 540, 944, 913, 820, 789, 696, 665, 572, 541, 976, 945, 914, 852, 821, 790, 728, 697, 666, 604, 573, 542, 1008, 977, 946, 915, 884, 853, 822, 791,
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760, 729, 698, 667, 636, 605, 574, 543, 1009, 978, 947, 885, 854, 823, 761, 730, 699, 637, 606, 575, 1010, 979, 886, 855, 762, 731, 638, 607, 1011, 887, 763, 639,
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916, 792, 668, 948, 917, 824, 793, 700, 669, 980, 949, 918, 856, 825, 794, 732, 701, 670, 1012, 981, 950, 919, 888, 857, 826, 795, 764, 733, 702, 671, 1013, 982,
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951, 889, 858, 827, 765, 734, 703, 1014, 983, 890, 859, 766, 735, 1015, 891, 767, 920, 796, 952, 921, 828, 797, 984, 953, 922, 860, 829, 798, 1016, 985, 954, 923,
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892, 861, 830, 799, 1017, 986, 955, 893, 862, 831, 1018, 987, 894, 863, 1019, 895, 924, 956, 925, 988, 957, 926, 1020, 989, 958, 927, 1021, 990, 959, 1022, 991, 1023,
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};
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/* Array indices are identical to previously-existing CONTEXT_NODE indices */
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const vp9_tree_index vp9_coef_tree[ 22] = /* corresponding _CONTEXT_NODEs */
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{
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-DCT_EOB_TOKEN, 2, /* 0 = EOB */
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-ZERO_TOKEN, 4, /* 1 = ZERO */
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-ONE_TOKEN, 6, /* 2 = ONE */
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8, 12, /* 3 = LOW_VAL */
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-TWO_TOKEN, 10, /* 4 = TWO */
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-THREE_TOKEN, -FOUR_TOKEN, /* 5 = THREE */
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14, 16, /* 6 = HIGH_LOW */
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-DCT_VAL_CATEGORY1, -DCT_VAL_CATEGORY2, /* 7 = CAT_ONE */
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18, 20, /* 8 = CAT_THREEFOUR */
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-DCT_VAL_CATEGORY3, -DCT_VAL_CATEGORY4, /* 9 = CAT_THREE */
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-DCT_VAL_CATEGORY5, -DCT_VAL_CATEGORY6 /* 10 = CAT_FIVE */
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};
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struct vp9_token vp9_coef_encodings[MAX_ENTROPY_TOKENS];
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/* Trees for extra bits. Probabilities are constant and
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do not depend on previously encoded bits */
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static const vp9_prob Pcat1[] = { 159};
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static const vp9_prob Pcat2[] = { 165, 145};
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static const vp9_prob Pcat3[] = { 173, 148, 140};
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static const vp9_prob Pcat4[] = { 176, 155, 140, 135};
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static const vp9_prob Pcat5[] = { 180, 157, 141, 134, 130};
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static const vp9_prob Pcat6[] = {
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254, 254, 254, 252, 249, 243, 230, 196, 177, 153, 140, 133, 130, 129
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};
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const vp9_tree_index vp9_coefmodel_tree[6] = {
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-DCT_EOB_MODEL_TOKEN, 2, /* 0 = EOB */
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-ZERO_TOKEN, 4, /* 1 = ZERO */
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-ONE_TOKEN, -TWO_TOKEN,
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};
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// Model obtained from a 2-sided zero-centerd distribuition derived
|
|
// from a Pareto distribution. The cdf of the distribution is:
|
|
// cdf(x) = 0.5 + 0.5 * sgn(x) * [1 - {alpha/(alpha + |x|)} ^ beta]
|
|
//
|
|
// For a given beta and a given probablity of the 1-node, the alpha
|
|
// is first solved, and then the {alpha, beta} pair is used to generate
|
|
// the probabilities for the rest of the nodes.
|
|
|
|
// beta = 8
|
|
static const vp9_prob modelcoefprobs_pareto8[COEFPROB_MODELS][MODEL_NODES] = {
|
|
{ 3, 86, 128, 6, 86, 23, 88, 29},
|
|
{ 9, 86, 129, 17, 88, 61, 94, 76},
|
|
{ 15, 87, 129, 28, 89, 93, 100, 110},
|
|
{ 20, 88, 130, 38, 91, 118, 106, 136},
|
|
{ 26, 89, 131, 48, 92, 139, 111, 156},
|
|
{ 31, 90, 131, 58, 94, 156, 117, 171},
|
|
{ 37, 90, 132, 66, 95, 171, 122, 184},
|
|
{ 42, 91, 132, 75, 97, 183, 127, 194},
|
|
{ 47, 92, 133, 83, 98, 193, 132, 202},
|
|
{ 52, 93, 133, 90, 100, 201, 137, 208},
|
|
{ 57, 94, 134, 98, 101, 208, 142, 214},
|
|
{ 62, 94, 135, 105, 103, 214, 146, 218},
|
|
{ 66, 95, 135, 111, 104, 219, 151, 222},
|
|
{ 71, 96, 136, 117, 106, 224, 155, 225},
|
|
{ 76, 97, 136, 123, 107, 227, 159, 228},
|
|
{ 80, 98, 137, 129, 109, 231, 162, 231},
|
|
{ 84, 98, 138, 134, 110, 234, 166, 233},
|
|
{ 89, 99, 138, 140, 112, 236, 170, 235},
|
|
{ 93, 100, 139, 145, 113, 238, 173, 236},
|
|
{ 97, 101, 140, 149, 115, 240, 176, 238},
|
|
{101, 102, 140, 154, 116, 242, 179, 239},
|
|
{105, 103, 141, 158, 118, 243, 182, 240},
|
|
{109, 104, 141, 162, 119, 244, 185, 241},
|
|
{113, 104, 142, 166, 120, 245, 187, 242},
|
|
{116, 105, 143, 170, 122, 246, 190, 243},
|
|
{120, 106, 143, 173, 123, 247, 192, 244},
|
|
{123, 107, 144, 177, 125, 248, 195, 244},
|
|
{127, 108, 145, 180, 126, 249, 197, 245},
|
|
{130, 109, 145, 183, 128, 249, 199, 245},
|
|
{134, 110, 146, 186, 129, 250, 201, 246},
|
|
{137, 111, 147, 189, 131, 251, 203, 246},
|
|
{140, 112, 147, 192, 132, 251, 205, 247},
|
|
{143, 113, 148, 194, 133, 251, 207, 247},
|
|
{146, 114, 149, 197, 135, 252, 208, 248},
|
|
{149, 115, 149, 199, 136, 252, 210, 248},
|
|
{152, 115, 150, 201, 138, 252, 211, 248},
|
|
{155, 116, 151, 204, 139, 253, 213, 249},
|
|
{158, 117, 151, 206, 140, 253, 214, 249},
|
|
{161, 118, 152, 208, 142, 253, 216, 249},
|
|
{163, 119, 153, 210, 143, 253, 217, 249},
|
|
{166, 120, 153, 212, 144, 254, 218, 250},
|
|
{168, 121, 154, 213, 146, 254, 220, 250},
|
|
{171, 122, 155, 215, 147, 254, 221, 250},
|
|
{173, 123, 155, 217, 148, 254, 222, 250},
|
|
{176, 124, 156, 218, 150, 254, 223, 250},
|
|
{178, 125, 157, 220, 151, 254, 224, 251},
|
|
{180, 126, 157, 221, 152, 254, 225, 251},
|
|
{183, 127, 158, 222, 153, 254, 226, 251},
|
|
{185, 128, 159, 224, 155, 255, 227, 251},
|
|
{187, 129, 160, 225, 156, 255, 228, 251},
|
|
{189, 131, 160, 226, 157, 255, 228, 251},
|
|
{191, 132, 161, 227, 159, 255, 229, 251},
|
|
{193, 133, 162, 228, 160, 255, 230, 252},
|
|
{195, 134, 163, 230, 161, 255, 231, 252},
|
|
{197, 135, 163, 231, 162, 255, 231, 252},
|
|
{199, 136, 164, 232, 163, 255, 232, 252},
|
|
{201, 137, 165, 233, 165, 255, 233, 252},
|
|
{202, 138, 166, 233, 166, 255, 233, 252},
|
|
{204, 139, 166, 234, 167, 255, 234, 252},
|
|
{206, 140, 167, 235, 168, 255, 235, 252},
|
|
{207, 141, 168, 236, 169, 255, 235, 252},
|
|
{209, 142, 169, 237, 171, 255, 236, 252},
|
|
{210, 144, 169, 237, 172, 255, 236, 252},
|
|
{212, 145, 170, 238, 173, 255, 237, 252},
|
|
{214, 146, 171, 239, 174, 255, 237, 253},
|
|
{215, 147, 172, 240, 175, 255, 238, 253},
|
|
{216, 148, 173, 240, 176, 255, 238, 253},
|
|
{218, 149, 173, 241, 177, 255, 239, 253},
|
|
{219, 150, 174, 241, 179, 255, 239, 253},
|
|
{220, 152, 175, 242, 180, 255, 240, 253},
|
|
{222, 153, 176, 242, 181, 255, 240, 253},
|
|
{223, 154, 177, 243, 182, 255, 240, 253},
|
|
{224, 155, 178, 244, 183, 255, 241, 253},
|
|
{225, 156, 178, 244, 184, 255, 241, 253},
|
|
{226, 158, 179, 244, 185, 255, 242, 253},
|
|
{228, 159, 180, 245, 186, 255, 242, 253},
|
|
{229, 160, 181, 245, 187, 255, 242, 253},
|
|
{230, 161, 182, 246, 188, 255, 243, 253},
|
|
{231, 163, 183, 246, 189, 255, 243, 253},
|
|
{232, 164, 184, 247, 190, 255, 243, 253},
|
|
{233, 165, 185, 247, 191, 255, 244, 253},
|
|
{234, 166, 185, 247, 192, 255, 244, 253},
|
|
{235, 168, 186, 248, 193, 255, 244, 253},
|
|
{236, 169, 187, 248, 194, 255, 244, 253},
|
|
{236, 170, 188, 248, 195, 255, 245, 253},
|
|
{237, 171, 189, 249, 196, 255, 245, 254},
|
|
{238, 173, 190, 249, 197, 255, 245, 254},
|
|
{239, 174, 191, 249, 198, 255, 245, 254},
|
|
{240, 175, 192, 249, 199, 255, 246, 254},
|
|
{240, 177, 193, 250, 200, 255, 246, 254},
|
|
{241, 178, 194, 250, 201, 255, 246, 254},
|
|
{242, 179, 195, 250, 202, 255, 246, 254},
|
|
{242, 181, 196, 250, 203, 255, 247, 254},
|
|
{243, 182, 197, 251, 204, 255, 247, 254},
|
|
{244, 184, 198, 251, 205, 255, 247, 254},
|
|
{244, 185, 199, 251, 206, 255, 247, 254},
|
|
{245, 186, 200, 251, 207, 255, 247, 254},
|
|
{246, 188, 201, 252, 207, 255, 248, 254},
|
|
{246, 189, 202, 252, 208, 255, 248, 254},
|
|
{247, 191, 203, 252, 209, 255, 248, 254},
|
|
{247, 192, 204, 252, 210, 255, 248, 254},
|
|
{248, 194, 205, 252, 211, 255, 248, 254},
|
|
{248, 195, 206, 252, 212, 255, 249, 254},
|
|
{249, 197, 207, 253, 213, 255, 249, 254},
|
|
{249, 198, 208, 253, 214, 255, 249, 254},
|
|
{250, 200, 210, 253, 215, 255, 249, 254},
|
|
{250, 201, 211, 253, 215, 255, 249, 254},
|
|
{250, 203, 212, 253, 216, 255, 249, 254},
|
|
{251, 204, 213, 253, 217, 255, 250, 254},
|
|
{251, 206, 214, 254, 218, 255, 250, 254},
|
|
{252, 207, 216, 254, 219, 255, 250, 254},
|
|
{252, 209, 217, 254, 220, 255, 250, 254},
|
|
{252, 211, 218, 254, 221, 255, 250, 254},
|
|
{253, 213, 219, 254, 222, 255, 250, 254},
|
|
{253, 214, 221, 254, 223, 255, 250, 254},
|
|
{253, 216, 222, 254, 224, 255, 251, 254},
|
|
{253, 218, 224, 254, 225, 255, 251, 254},
|
|
{254, 220, 225, 254, 225, 255, 251, 254},
|
|
{254, 222, 227, 255, 226, 255, 251, 254},
|
|
{254, 224, 228, 255, 227, 255, 251, 254},
|
|
{254, 226, 230, 255, 228, 255, 251, 254},
|
|
{255, 228, 231, 255, 230, 255, 251, 254},
|
|
{255, 230, 233, 255, 231, 255, 252, 254},
|
|
{255, 232, 235, 255, 232, 255, 252, 254},
|
|
{255, 235, 237, 255, 233, 255, 252, 254},
|
|
{255, 238, 240, 255, 235, 255, 252, 255},
|
|
{255, 241, 243, 255, 236, 255, 252, 254},
|
|
{255, 246, 247, 255, 239, 255, 253, 255}
|
|
};
|
|
|
|
static void extend_model_to_full_distribution(vp9_prob p,
|
|
vp9_prob *tree_probs) {
|
|
const int l = ((p - 1) / 2);
|
|
const vp9_prob (*model)[MODEL_NODES] = modelcoefprobs_pareto8;
|
|
if (p & 1) {
|
|
vpx_memcpy(tree_probs + UNCONSTRAINED_NODES,
|
|
model[l], MODEL_NODES * sizeof(vp9_prob));
|
|
} else {
|
|
// interpolate
|
|
int i;
|
|
for (i = UNCONSTRAINED_NODES; i < ENTROPY_NODES; ++i)
|
|
tree_probs[i] = (model[l][i - UNCONSTRAINED_NODES] +
|
|
model[l + 1][i - UNCONSTRAINED_NODES]) >> 1;
|
|
}
|
|
}
|
|
|
|
void vp9_model_to_full_probs(const vp9_prob *model, vp9_prob *full) {
|
|
if (full != model)
|
|
vpx_memcpy(full, model, sizeof(vp9_prob) * UNCONSTRAINED_NODES);
|
|
extend_model_to_full_distribution(model[PIVOT_NODE], full);
|
|
}
|
|
|
|
static vp9_tree_index cat1[2], cat2[4], cat3[6], cat4[8], cat5[10], cat6[28];
|
|
|
|
static void init_bit_tree(vp9_tree_index *p, int n) {
|
|
int i = 0;
|
|
|
|
while (++i < n) {
|
|
p[0] = p[1] = i << 1;
|
|
p += 2;
|
|
}
|
|
|
|
p[0] = p[1] = 0;
|
|
}
|
|
|
|
static void init_bit_trees() {
|
|
init_bit_tree(cat1, 1);
|
|
init_bit_tree(cat2, 2);
|
|
init_bit_tree(cat3, 3);
|
|
init_bit_tree(cat4, 4);
|
|
init_bit_tree(cat5, 5);
|
|
init_bit_tree(cat6, 14);
|
|
}
|
|
|
|
vp9_extra_bit vp9_extra_bits[12] = {
|
|
{ 0, 0, 0, 0},
|
|
{ 0, 0, 0, 1},
|
|
{ 0, 0, 0, 2},
|
|
{ 0, 0, 0, 3},
|
|
{ 0, 0, 0, 4},
|
|
{ cat1, Pcat1, 1, 5},
|
|
{ cat2, Pcat2, 2, 7},
|
|
{ cat3, Pcat3, 3, 11},
|
|
{ cat4, Pcat4, 4, 19},
|
|
{ cat5, Pcat5, 5, 35},
|
|
{ cat6, Pcat6, 14, 67},
|
|
{ 0, 0, 0, 0}
|
|
};
|
|
|
|
#include "vp9/common/vp9_default_coef_probs.h"
|
|
|
|
void vp9_default_coef_probs(VP9_COMMON *pc) {
|
|
vp9_copy(pc->fc.coef_probs[TX_4X4], default_coef_probs_4x4);
|
|
vp9_copy(pc->fc.coef_probs[TX_8X8], default_coef_probs_8x8);
|
|
vp9_copy(pc->fc.coef_probs[TX_16X16], default_coef_probs_16x16);
|
|
vp9_copy(pc->fc.coef_probs[TX_32X32], default_coef_probs_32x32);
|
|
}
|
|
|
|
// Neighborhood 5-tuples for various scans and blocksizes,
|
|
// in {top, left, topleft, topright, bottomleft} order
|
|
// for each position in raster scan order.
|
|
// -1 indicates the neighbor does not exist.
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_default_scan_4x4_neighbors[17 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_col_scan_4x4_neighbors[17 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_row_scan_4x4_neighbors[17 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_col_scan_8x8_neighbors[65 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_row_scan_8x8_neighbors[65 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_default_scan_8x8_neighbors[65 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_col_scan_16x16_neighbors[257 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_row_scan_16x16_neighbors[257 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_default_scan_16x16_neighbors[257 * MAX_NEIGHBORS]);
|
|
DECLARE_ALIGNED(16, int16_t,
|
|
vp9_default_scan_32x32_neighbors[1025 * MAX_NEIGHBORS]);
|
|
|
|
DECLARE_ALIGNED(16, int16_t, vp9_default_iscan_4x4[16]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_col_iscan_4x4[16]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_row_iscan_4x4[16]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_col_iscan_8x8[64]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_row_iscan_8x8[64]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_default_iscan_8x8[64]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_col_iscan_16x16[256]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_row_iscan_16x16[256]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_default_iscan_16x16[256]);
|
|
DECLARE_ALIGNED(16, int16_t, vp9_default_iscan_32x32[1024]);
|
|
|
|
static int find_in_scan(const int16_t *scan, int l, int idx) {
|
|
int n, l2 = l * l;
|
|
for (n = 0; n < l2; n++) {
|
|
int rc = scan[n];
|
|
if (rc == idx)
|
|
return n;
|
|
}
|
|
assert(0);
|
|
return -1;
|
|
}
|
|
static void init_scan_neighbors(const int16_t *scan,
|
|
int16_t *iscan,
|
|
int l, int16_t *neighbors) {
|
|
int l2 = l * l;
|
|
int n, i, j;
|
|
|
|
// dc doesn't use this type of prediction
|
|
neighbors[MAX_NEIGHBORS * 0 + 0] = 0;
|
|
neighbors[MAX_NEIGHBORS * 0 + 1] = 0;
|
|
iscan[0] = find_in_scan(scan, l, 0);
|
|
for (n = 1; n < l2; n++) {
|
|
int rc = scan[n];
|
|
iscan[n] = find_in_scan(scan, l, n);
|
|
i = rc / l;
|
|
j = rc % l;
|
|
if (i > 0 && j > 0) {
|
|
// col/row scan is used for adst/dct, and generally means that
|
|
// energy decreases to zero much faster in the dimension in
|
|
// which ADST is used compared to the direction in which DCT
|
|
// is used. Likewise, we find much higher correlation between
|
|
// coefficients within the direction in which DCT is used.
|
|
// Therefore, if we use ADST/DCT, prefer the DCT neighbor coeff
|
|
// as a context. If ADST or DCT is used in both directions, we
|
|
// use the combination of the two as a context.
|
|
int a = (i - 1) * l + j;
|
|
int b = i * l + j - 1;
|
|
if (scan == vp9_col_scan_4x4 || scan == vp9_col_scan_8x8 ||
|
|
scan == vp9_col_scan_16x16) {
|
|
// in the col/row scan cases (as well as left/top edge cases), we set
|
|
// both contexts to the same value, so we can branchlessly do a+b+1>>1
|
|
// which automatically becomes a if a == b
|
|
neighbors[MAX_NEIGHBORS * n + 0] =
|
|
neighbors[MAX_NEIGHBORS * n + 1] = a;
|
|
} else if (scan == vp9_row_scan_4x4 || scan == vp9_row_scan_8x8 ||
|
|
scan == vp9_row_scan_16x16) {
|
|
neighbors[MAX_NEIGHBORS * n + 0] =
|
|
neighbors[MAX_NEIGHBORS * n + 1] = b;
|
|
} else {
|
|
neighbors[MAX_NEIGHBORS * n + 0] = a;
|
|
neighbors[MAX_NEIGHBORS * n + 1] = b;
|
|
}
|
|
} else if (i > 0) {
|
|
neighbors[MAX_NEIGHBORS * n + 0] =
|
|
neighbors[MAX_NEIGHBORS * n + 1] = (i - 1) * l + j;
|
|
} else {
|
|
assert(j > 0);
|
|
neighbors[MAX_NEIGHBORS * n + 0] =
|
|
neighbors[MAX_NEIGHBORS * n + 1] = i * l + j - 1;
|
|
}
|
|
assert(iscan[neighbors[MAX_NEIGHBORS * n + 0]] < n);
|
|
}
|
|
// one padding item so we don't have to add branches in code to handle
|
|
// calls to get_coef_context() for the token after the final dc token
|
|
neighbors[MAX_NEIGHBORS * l2 + 0] = 0;
|
|
neighbors[MAX_NEIGHBORS * l2 + 1] = 0;
|
|
}
|
|
|
|
void vp9_init_neighbors() {
|
|
init_scan_neighbors(vp9_default_scan_4x4, vp9_default_iscan_4x4, 4,
|
|
vp9_default_scan_4x4_neighbors);
|
|
init_scan_neighbors(vp9_row_scan_4x4, vp9_row_iscan_4x4, 4,
|
|
vp9_row_scan_4x4_neighbors);
|
|
init_scan_neighbors(vp9_col_scan_4x4, vp9_col_iscan_4x4, 4,
|
|
vp9_col_scan_4x4_neighbors);
|
|
init_scan_neighbors(vp9_default_scan_8x8, vp9_default_iscan_8x8, 8,
|
|
vp9_default_scan_8x8_neighbors);
|
|
init_scan_neighbors(vp9_row_scan_8x8, vp9_row_iscan_8x8, 8,
|
|
vp9_row_scan_8x8_neighbors);
|
|
init_scan_neighbors(vp9_col_scan_8x8, vp9_col_iscan_8x8, 8,
|
|
vp9_col_scan_8x8_neighbors);
|
|
init_scan_neighbors(vp9_default_scan_16x16, vp9_default_iscan_16x16, 16,
|
|
vp9_default_scan_16x16_neighbors);
|
|
init_scan_neighbors(vp9_row_scan_16x16, vp9_row_iscan_16x16, 16,
|
|
vp9_row_scan_16x16_neighbors);
|
|
init_scan_neighbors(vp9_col_scan_16x16, vp9_col_iscan_16x16, 16,
|
|
vp9_col_scan_16x16_neighbors);
|
|
init_scan_neighbors(vp9_default_scan_32x32, vp9_default_iscan_32x32, 32,
|
|
vp9_default_scan_32x32_neighbors);
|
|
}
|
|
|
|
const int16_t *vp9_get_coef_neighbors_handle(const int16_t *scan) {
|
|
if (scan == vp9_default_scan_4x4) {
|
|
return vp9_default_scan_4x4_neighbors;
|
|
} else if (scan == vp9_row_scan_4x4) {
|
|
return vp9_row_scan_4x4_neighbors;
|
|
} else if (scan == vp9_col_scan_4x4) {
|
|
return vp9_col_scan_4x4_neighbors;
|
|
} else if (scan == vp9_default_scan_8x8) {
|
|
return vp9_default_scan_8x8_neighbors;
|
|
} else if (scan == vp9_row_scan_8x8) {
|
|
return vp9_row_scan_8x8_neighbors;
|
|
} else if (scan == vp9_col_scan_8x8) {
|
|
return vp9_col_scan_8x8_neighbors;
|
|
} else if (scan == vp9_default_scan_16x16) {
|
|
return vp9_default_scan_16x16_neighbors;
|
|
} else if (scan == vp9_row_scan_16x16) {
|
|
return vp9_row_scan_16x16_neighbors;
|
|
} else if (scan == vp9_col_scan_16x16) {
|
|
return vp9_col_scan_16x16_neighbors;
|
|
} else {
|
|
assert(scan == vp9_default_scan_32x32);
|
|
return vp9_default_scan_32x32_neighbors;
|
|
}
|
|
}
|
|
|
|
void vp9_coef_tree_initialize() {
|
|
vp9_init_neighbors();
|
|
init_bit_trees();
|
|
vp9_tokens_from_tree(vp9_coef_encodings, vp9_coef_tree);
|
|
}
|
|
|
|
// #define COEF_COUNT_TESTING
|
|
|
|
#define COEF_COUNT_SAT 24
|
|
#define COEF_MAX_UPDATE_FACTOR 112
|
|
#define COEF_COUNT_SAT_KEY 24
|
|
#define COEF_MAX_UPDATE_FACTOR_KEY 112
|
|
#define COEF_COUNT_SAT_AFTER_KEY 24
|
|
#define COEF_MAX_UPDATE_FACTOR_AFTER_KEY 128
|
|
|
|
static void adapt_coef_probs(VP9_COMMON *cm, TX_SIZE txfm_size,
|
|
unsigned int count_sat,
|
|
unsigned int update_factor) {
|
|
FRAME_CONTEXT *pre_fc = &cm->frame_contexts[cm->frame_context_idx];
|
|
|
|
vp9_coeff_probs_model *dst_coef_probs = cm->fc.coef_probs[txfm_size];
|
|
vp9_coeff_probs_model *pre_coef_probs = pre_fc->coef_probs[txfm_size];
|
|
vp9_coeff_count_model *coef_counts = cm->counts.coef[txfm_size];
|
|
unsigned int (*eob_branch_count)[REF_TYPES][COEF_BANDS][PREV_COEF_CONTEXTS] =
|
|
cm->counts.eob_branch[txfm_size];
|
|
int t, i, j, k, l;
|
|
unsigned int branch_ct[UNCONSTRAINED_NODES][2];
|
|
vp9_prob coef_probs[UNCONSTRAINED_NODES];
|
|
int entropy_nodes_adapt = UNCONSTRAINED_NODES;
|
|
|
|
for (i = 0; i < BLOCK_TYPES; ++i)
|
|
for (j = 0; j < REF_TYPES; ++j)
|
|
for (k = 0; k < COEF_BANDS; ++k)
|
|
for (l = 0; l < PREV_COEF_CONTEXTS; ++l) {
|
|
if (l >= 3 && k == 0)
|
|
continue;
|
|
vp9_tree_probs_from_distribution(vp9_coefmodel_tree, coef_probs,
|
|
branch_ct, coef_counts[i][j][k][l],
|
|
0);
|
|
branch_ct[0][1] = eob_branch_count[i][j][k][l] - branch_ct[0][0];
|
|
coef_probs[0] = get_binary_prob(branch_ct[0][0], branch_ct[0][1]);
|
|
for (t = 0; t < entropy_nodes_adapt; ++t)
|
|
dst_coef_probs[i][j][k][l][t] = merge_probs(
|
|
pre_coef_probs[i][j][k][l][t], coef_probs[t],
|
|
branch_ct[t], count_sat, update_factor);
|
|
}
|
|
}
|
|
|
|
void vp9_adapt_coef_probs(VP9_COMMON *cm) {
|
|
TX_SIZE t;
|
|
unsigned int count_sat, update_factor;
|
|
|
|
if (cm->frame_type == KEY_FRAME || cm->intra_only) {
|
|
update_factor = COEF_MAX_UPDATE_FACTOR_KEY;
|
|
count_sat = COEF_COUNT_SAT_KEY;
|
|
} else if (cm->last_frame_type == KEY_FRAME) {
|
|
update_factor = COEF_MAX_UPDATE_FACTOR_AFTER_KEY; /* adapt quickly */
|
|
count_sat = COEF_COUNT_SAT_AFTER_KEY;
|
|
} else {
|
|
update_factor = COEF_MAX_UPDATE_FACTOR;
|
|
count_sat = COEF_COUNT_SAT;
|
|
}
|
|
for (t = TX_4X4; t <= TX_32X32; t++)
|
|
adapt_coef_probs(cm, t, count_sat, update_factor);
|
|
}
|