void vp10_enable_segfeature(struct segmentation *seg, int segment_id,
SEG_LVL_FEATURES feature_id) {
seg->feature_mask[segment_id] |= 1 << feature_id;
}
int vp10_seg_feature_data_max(SEG_LVL_FEATURES feature_id) {
return seg_feature_data_max[feature_id];
}
int vp10_is_segfeature_signed(SEG_LVL_FEATURES feature_id) {
return seg_feature_data_signed[feature_id];
}
void vp10_set_segdata(struct segmentation *seg, int segment_id,
SEG_LVL_FEATURES feature_id, int seg_data) {
assert(seg_data <= seg_feature_data_max[feature_id]);
if (seg_data < 0) {
assert(seg_feature_data_signed[feature_id]);
assert(-seg_data <= seg_feature_data_max[feature_id]);
}
seg->feature_data[segment_id][feature_id] = seg_data;
}
const vpx_tree_index vp10_segment_tree[TREE_SIZE(MAX_SEGMENTS)] = {
2, 4, 6, 8, 10, 12, 0, -1, -2, -3, -4, -5, -6, -7
};
// TBD? Functions to read and write segment data with range / validity checking
{ 10, 7, 6 }, // a/l both split
};
static const vp9_prob default_inter_mode_probs[INTER_MODE_CONTEXTS]
[INTER_MODES - 1] = {
{2, 173, 34}, // 0 = both zero mv
{7, 145, 85}, // 1 = one zero mv + one a predicted mv
{7, 166, 63}, // 2 = two predicted mvs
{7, 94, 66}, // 3 = one predicted/zero and one new mv
{8, 64, 46}, // 4 = two new mvs
{17, 81, 31}, // 5 = one intra neighbour + x
{25, 29, 30}, // 6 = two intra neighbours
};
/* Array indices are identical to previously-existing INTRAMODECONTEXTNODES. */
const vp9_tree_index vp9_intra_mode_tree[TREE_SIZE(INTRA_MODES)] = {
-DC_PRED, 2, /* 0 = DC_NODE */
-TM_PRED, 4, /* 1 = TM_NODE */
-V_PRED, 6, /* 2 = V_NODE */
8, 12, /* 3 = COM_NODE */
-H_PRED, 10, /* 4 = H_NODE */
-D135_PRED, -D117_PRED, /* 5 = D135_NODE */
-D45_PRED, 14, /* 6 = D45_NODE */
-D63_PRED, 16, /* 7 = D63_NODE */
-D153_PRED, -D207_PRED /* 8 = D153_NODE */
};
struct vp9_token vp9_intra_mode_encodings[INTRA_MODES];
const vp9_tree_index vp9_inter_mode_tree[TREE_SIZE(INTER_MODES)] = {
-INTER_OFFSET(ZEROMV), 2,
-INTER_OFFSET(NEARESTMV), 4,
{7, 0}, {7, 2}, {7, 4}, {7, 6}, {7, 8}, {7, 10}, {7, 12}, {7, 14},
{8, 0}, {8, 2}, {8, 4}, {8, 6}, {8, 8}, {8, 10}, {8, 12},
{8, 14}, {8, 16}, {8, 18}, {8, 20}, {8, 22}, {8, 24},
{8, 26}, {8, 28}, {8, 30}, {9, 0}, {9, 2},
{9, 4}, {9, 6}, {9, 8}, {9, 10}, {9, 12}, {9, 14}, {9, 16},
{9, 18}, {9, 20}, {9, 22}, {9, 24}, {9, 26}, {9, 28},
{9, 30}, {9, 32}, {9, 34}, {9, 36}, {9, 38}, {9, 40},
{9, 42}, {9, 44}, {9, 46}, {9, 48}, {9, 50}, {9, 52},
{9, 54}, {9, 56}, {9, 58}, {9, 60}, {9, 62}
};
const TOKENVALUE *vp9_dct_cat_lt_10_value_tokens = dct_cat_lt_10_value_tokens +
(sizeof(dct_cat_lt_10_value_tokens) / sizeof(*dct_cat_lt_10_value_tokens))
/ 2;
// Array indices are identical to previously-existing CONTEXT_NODE indices
const vp9_tree_index vp9_coef_tree[TREE_SIZE(ENTROPY_TOKENS)] = {
-EOB_TOKEN, 2, // 0 = EOB
-ZERO_TOKEN, 4, // 1 = ZERO
-ONE_TOKEN, 6, // 2 = ONE
8, 12, // 3 = LOW_VAL
-TWO_TOKEN, 10, // 4 = TWO
-THREE_TOKEN, -FOUR_TOKEN, // 5 = THREE
14, 16, // 6 = HIGH_LOW
-CATEGORY1_TOKEN, -CATEGORY2_TOKEN, // 7 = CAT_ONE
18, 20, // 8 = CAT_THREEFOUR
-CATEGORY3_TOKEN, -CATEGORY4_TOKEN, // 9 = CAT_THREE
-CATEGORY5_TOKEN, -CATEGORY6_TOKEN // 10 = CAT_FIVE
};
// Unconstrained Node Tree
const vp9_tree_index vp9_coef_con_tree[TREE_SIZE(ENTROPY_TOKENS)] = {
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE 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.
*/
#include "vp10/common/entropy.h"
#include "vp10/common/blockd.h"
#include "vp10/common/onyxc_int.h"
#include "vp10/common/entropymode.h"
#include "vpx_mem/vpx_mem.h"
#include "vpx/vpx_integer.h"
// Unconstrained Node Tree
const vpx_tree_index vp10_coef_con_tree[TREE_SIZE(ENTROPY_TOKENS)] = {
2, 6, // 0 = LOW_VAL
-TWO_TOKEN, 4, // 1 = TWO
-THREE_TOKEN, -FOUR_TOKEN, // 2 = THREE
8, 10, // 3 = HIGH_LOW
-CATEGORY1_TOKEN, -CATEGORY2_TOKEN, // 4 = CAT_ONE
12, 14, // 5 = CAT_THREEFOUR
-CATEGORY3_TOKEN, -CATEGORY4_TOKEN, // 6 = CAT_THREE
-CATEGORY5_TOKEN, -CATEGORY6_TOKEN // 7 = CAT_FIVE
};
const vpx_prob vp10_cat1_prob[] = { 159 };
const vpx_prob vp10_cat2_prob[] = { 165, 145 };
const vpx_prob vp10_cat3_prob[] = { 173, 148, 140 };
const vpx_prob vp10_cat4_prob[] = { 176, 155, 140, 135 };
const vpx_prob vp10_cat5_prob[] = { 180, 157, 141, 134, 130 };
void av1_enable_segfeature(struct segmentation *seg, int segment_id,
SEG_LVL_FEATURES feature_id) {
seg->feature_mask[segment_id] |= 1 << feature_id;
}
int av1_seg_feature_data_max(SEG_LVL_FEATURES feature_id) {
return seg_feature_data_max[feature_id];
}
int av1_is_segfeature_signed(SEG_LVL_FEATURES feature_id) {
return seg_feature_data_signed[feature_id];
}
void av1_set_segdata(struct segmentation *seg, int segment_id,
SEG_LVL_FEATURES feature_id, int seg_data) {
assert(seg_data <= seg_feature_data_max[feature_id]);
if (seg_data < 0) {
assert(seg_feature_data_signed[feature_id]);
assert(-seg_data <= seg_feature_data_max[feature_id]);
}
seg->feature_data[segment_id][feature_id] = seg_data;
}
const aom_tree_index av1_segment_tree[TREE_SIZE(MAX_SEGMENTS)] = {
2, 4, 6, 8, 10, 12, 0, -1, -2, -3, -4, -5, -6, -7
};
// TBD? Functions to read and write segment data with range / validity checking
{ 7, 0 }, { 7, 2 }, { 7, 4 }, { 7, 6 }, { 7, 8 }, { 7, 10 }, { 7, 12 },
{ 7, 14 }, { 8, 0 }, { 8, 2 }, { 8, 4 }, { 8, 6 }, { 8, 8 }, { 8, 10 },
{ 8, 12 }, { 8, 14 }, { 8, 16 }, { 8, 18 }, { 8, 20 }, { 8, 22 }, { 8, 24 },
{ 8, 26 }, { 8, 28 }, { 8, 30 }, { 9, 0 }, { 9, 2 }, { 9, 4 }, { 9, 6 },
{ 9, 8 }, { 9, 10 }, { 9, 12 }, { 9, 14 }, { 9, 16 }, { 9, 18 }, { 9, 20 },
{ 9, 22 }, { 9, 24 }, { 9, 26 }, { 9, 28 }, { 9, 30 }, { 9, 32 }, { 9, 34 },
{ 9, 36 }, { 9, 38 }, { 9, 40 }, { 9, 42 }, { 9, 44 }, { 9, 46 }, { 9, 48 },
{ 9, 50 }, { 9, 52 }, { 9, 54 }, { 9, 56 }, { 9, 58 }, { 9, 60 }, { 9, 62 }
};
const TOKENVALUE *av1_dct_cat_lt_10_value_tokens =
dct_cat_lt_10_value_tokens +
(sizeof(dct_cat_lt_10_value_tokens) / sizeof(*dct_cat_lt_10_value_tokens)) /
2;
// Array indices are identical to previously-existing CONTEXT_NODE indices
const aom_tree_index av1_coef_tree[TREE_SIZE(ENTROPY_TOKENS)] = {
-EOB_TOKEN,
2, // 0 = EOB
-ZERO_TOKEN,
4, // 1 = ZERO
-ONE_TOKEN,
6, // 2 = ONE
8,
12, // 3 = LOW_VAL
-TWO_TOKEN,
10, // 4 = TWO
-THREE_TOKEN,
-FOUR_TOKEN, // 5 = THREE
14,
16, // 6 = HIGH_LOW
-CATEGORY1_TOKEN,
{ 10, 7, 6 }, // a/l both split
};
static const vpx_prob default_inter_mode_probs[INTER_MODE_CONTEXTS]
[INTER_MODES - 1] = {
{2, 173, 34}, // 0 = both zero mv
{7, 145, 85}, // 1 = one zero mv + one a predicted mv
{7, 166, 63}, // 2 = two predicted mvs
{7, 94, 66}, // 3 = one predicted/zero and one new mv
{8, 64, 46}, // 4 = two new mvs
{17, 81, 31}, // 5 = one intra neighbour + x
{25, 29, 30}, // 6 = two intra neighbours
};
/* Array indices are identical to previously-existing INTRAMODECONTEXTNODES. */
const vpx_tree_index vp10_intra_mode_tree[TREE_SIZE(INTRA_MODES)] = {
-DC_PRED, 2, /* 0 = DC_NODE */
-TM_PRED, 4, /* 1 = TM_NODE */
-V_PRED, 6, /* 2 = V_NODE */
8, 12, /* 3 = COM_NODE */
-H_PRED, 10, /* 4 = H_NODE */
-D135_PRED, -D117_PRED, /* 5 = D135_NODE */
-D45_PRED, 14, /* 6 = D45_NODE */
-D63_PRED, 16, /* 7 = D63_NODE */
-D153_PRED, -D207_PRED /* 8 = D153_NODE */
};
const vpx_tree_index vp10_inter_mode_tree[TREE_SIZE(INTER_MODES)] = {
-INTER_OFFSET(ZEROMV), 2,
-INTER_OFFSET(NEARESTMV), 4,
-INTER_OFFSET(NEARMV), -INTER_OFFSET(NEWMV)
#define CAT_FIVE_CONTEXT_NODE 7
#define INCREMENT_COUNT(token) \
do { \
if (!cm->frame_parallel_decoding_mode) \
++coef_counts[band][ctx][token]; \
} while (0)
static INLINE int read_coeff(const vp9_prob *probs, int n, vp9_reader *r) {
int i, val = 0;
for (i = 0; i < n; ++i)
val = (val << 1) | vp9_read(r, probs[i]);
return val;
}
static const vp9_tree_index coeff_subtree_high[TREE_SIZE(ENTROPY_TOKENS)] = {
2, 6, /* 0 = LOW_VAL */
-TWO_TOKEN, 4, /* 1 = TWO */
-THREE_TOKEN, -FOUR_TOKEN, /* 2 = THREE */
8, 10, /* 3 = HIGH_LOW */
-CATEGORY1_TOKEN, -CATEGORY2_TOKEN, /* 4 = CAT_ONE */
12, 14, /* 5 = CAT_THREEFOUR */
-CATEGORY3_TOKEN, -CATEGORY4_TOKEN, /* 6 = CAT_THREE */
-CATEGORY5_TOKEN, -CATEGORY6_TOKEN /* 7 = CAT_FIVE */
};
static int decode_coefs(VP9_COMMON *cm, const MACROBLOCKD *xd,
FRAME_COUNTS *counts, PLANE_TYPE type,
tran_low_t *dqcoeff, TX_SIZE tx_size, const int16_t *dq,
int ctx, const int16_t *scan, const int16_t *nb,
vp9_reader *r) {
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
};
const uint8_t vp9_coefband_trans_4x4[16] = {
0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5,
};
const uint8_t vp9_pt_energy_class[ENTROPY_TOKENS] = {
0, 1, 2, 3, 3, 4, 4, 5, 5, 5, 5, 5
};
const vp9_tree_index vp9_coefmodel_tree[TREE_SIZE(UNCONSTRAINED_NODES + 1)] = {
-EOB_MODEL_TOKEN, 2,
-ZERO_TOKEN, 4,
-ONE_TOKEN, -TWO_TOKEN,
};
// 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
