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/*
* Copyright (c) 2021, Alliance for Open Media. All rights reserved
*
* This source code is subject to the terms of the BSD 3-Clause Clear License
* and the Alliance for Open Media Patent License 1.0. If the BSD 3-Clause Clear
* License was not distributed with this source code in the LICENSE file, you
* can obtain it at aomedia.org/license/software-license/bsd-3-c-c/. If the
* Alliance for Open Media Patent License 1.0 was not distributed with this
* source code in the PATENTS file, you can obtain it at
* aomedia.org/license/patent-license/.
*/
#include <math.h>
#include <stdlib.h>
#include "av1/common/pred_common.h"
#include "av1/encoder/block.h"
#include "av1/encoder/cost.h"
#include "av1/encoder/encoder.h"
#include "av1/encoder/intra_mode_search.h"
#include "av1/encoder/intra_mode_search_utils.h"
#include "av1/encoder/palette.h"
#include "av1/encoder/random.h"
#include "av1/encoder/rdopt_utils.h"
#include "av1/encoder/tx_search.h"
#define AV1_K_MEANS_DIM 1
#include "av1/encoder/k_means_template.h"
#undef AV1_K_MEANS_DIM
#define AV1_K_MEANS_DIM 2
#include "av1/encoder/k_means_template.h"
#undef AV1_K_MEANS_DIM
static int int_comparer(const void *a, const void *b) {
return (*(int *)a - *(int *)b);
}
int av1_remove_duplicates(int *centroids, int num_centroids) {
int num_unique; // number of unique centroids
int i;
qsort(centroids, num_centroids, sizeof(*centroids), int_comparer);
// Remove duplicates.
num_unique = 1;
for (i = 1; i < num_centroids; ++i) {
if (centroids[i] != centroids[i - 1]) { // found a new unique centroid
centroids[num_unique++] = centroids[i];
}
}
return num_unique;
}
static int delta_encode_cost(const int *colors, int num, int bit_depth,
int min_val) {
if (num <= 0) return 0;
int bits_cost = bit_depth;
if (num == 1) return bits_cost;
bits_cost += 2;
int max_delta = 0;
int deltas[PALETTE_MAX_SIZE];
const int min_bits = bit_depth - 3;
for (int i = 1; i < num; ++i) {
const int delta = colors[i] - colors[i - 1];
deltas[i - 1] = delta;
assert(delta >= min_val);
if (delta > max_delta) max_delta = delta;
}
int bits_per_delta = AOMMAX(av1_ceil_log2(max_delta + 1 - min_val), min_bits);
assert(bits_per_delta <= bit_depth);
int range = (1 << bit_depth) - colors[0] - min_val;
for (int i = 0; i < num - 1; ++i) {
bits_cost += bits_per_delta;
range -= deltas[i];
bits_per_delta = AOMMIN(bits_per_delta, av1_ceil_log2(range));
}
return bits_cost;
}
int av1_index_color_cache(const uint16_t *color_cache, int n_cache,
const uint16_t *colors, int n_colors,
uint8_t *cache_color_found, int *out_cache_colors) {
if (n_cache <= 0) {
for (int i = 0; i < n_colors; ++i) out_cache_colors[i] = colors[i];
return n_colors;
}
memset(cache_color_found, 0, n_cache * sizeof(*cache_color_found));
int n_in_cache = 0;
int in_cache_flags[PALETTE_MAX_SIZE];
memset(in_cache_flags, 0, sizeof(in_cache_flags));
#if CONFIG_INDEP_PALETTE_PARSING
for (int i = 0; i < n_cache; ++i) {
int duplicate = 0;
for (int j = 0; j < i; j++) {
if (color_cache[i] == color_cache[j]) duplicate = 1;
}
if (duplicate) continue;
#else
for (int i = 0; i < n_cache && n_in_cache < n_colors; ++i) {
#endif // CONFIG_INDEP_PALETTE_PARSING
for (int j = 0; j < n_colors; ++j) {
if (colors[j] == color_cache[i]) {
in_cache_flags[j] = 1;
cache_color_found[i] = 1;
++n_in_cache;
break;
}
}
}
int j = 0;
for (int i = 0; i < n_colors; ++i)
if (!in_cache_flags[i]) out_cache_colors[j++] = colors[i];
assert(j == n_colors - n_in_cache);
return j;
}
int av1_get_palette_delta_bits_v(const PALETTE_MODE_INFO *const pmi,
int bit_depth, int *zero_count,
int *min_bits) {
const int n = pmi->palette_size[1];
const int max_val = 1 << bit_depth;
int max_d = 0;
*min_bits = bit_depth - 4;
*zero_count = 0;
for (int i = 1; i < n; ++i) {
const int delta = pmi->palette_colors[2 * PALETTE_MAX_SIZE + i] -
pmi->palette_colors[2 * PALETTE_MAX_SIZE + i - 1];
const int v = abs(delta);
const int d = AOMMIN(v, max_val - v);
if (d > max_d) max_d = d;
if (d == 0) ++(*zero_count);
}
return AOMMAX(av1_ceil_log2(max_d + 1), *min_bits);
}
int av1_palette_color_cost_y(const PALETTE_MODE_INFO *const pmi,
const uint16_t *color_cache, int n_cache,
int bit_depth) {
const int n = pmi->palette_size[0];
int out_cache_colors[PALETTE_MAX_SIZE];
uint8_t cache_color_found[2 * PALETTE_MAX_SIZE];
const int n_out_cache =
av1_index_color_cache(color_cache, n_cache, pmi->palette_colors, n,
cache_color_found, out_cache_colors);
const int total_bits =
n_cache + delta_encode_cost(out_cache_colors, n_out_cache, bit_depth, 1);
return av1_cost_literal(total_bits);
}
int av1_palette_color_cost_uv(const PALETTE_MODE_INFO *const pmi,
const uint16_t *color_cache, int n_cache,
int bit_depth) {
const int n = pmi->palette_size[1];
int total_bits = 0;
// U channel palette color cost.
int out_cache_colors[PALETTE_MAX_SIZE];
uint8_t cache_color_found[2 * PALETTE_MAX_SIZE];
const int n_out_cache = av1_index_color_cache(
color_cache, n_cache, pmi->palette_colors + PALETTE_MAX_SIZE, n,
cache_color_found, out_cache_colors);
total_bits +=
n_cache + delta_encode_cost(out_cache_colors, n_out_cache, bit_depth, 0);
// V channel palette color cost.
int zero_count = 0, min_bits_v = 0;
const int bits_v =
av1_get_palette_delta_bits_v(pmi, bit_depth, &zero_count, &min_bits_v);
const int bits_using_delta =
2 + bit_depth + (bits_v + 1) * (n - 1) - zero_count;
const int bits_using_raw = bit_depth * n;
total_bits += 1 + AOMMIN(bits_using_delta, bits_using_raw);
return av1_cost_literal(total_bits);
}
// Extends 'color_map' array from 'orig_width x orig_height' to 'new_width x
// new_height'. Extra rows and columns are filled in by copying last valid
// row/column.
static AOM_INLINE void extend_palette_color_map(uint8_t *const color_map,
int orig_width, int orig_height,
int new_width, int new_height) {
int j;
assert(new_width >= orig_width);
assert(new_height >= orig_height);
if (new_width == orig_width && new_height == orig_height) return;
for (j = orig_height - 1; j >= 0; --j) {
memmove(color_map + j * new_width, color_map + j * orig_width, orig_width);
// Copy last column to extra columns.
memset(color_map + j * new_width + orig_width,
color_map[j * new_width + orig_width - 1], new_width - orig_width);
}
// Copy last row to extra rows.
for (j = orig_height; j < new_height; ++j) {
memcpy(color_map + j * new_width, color_map + (orig_height - 1) * new_width,
new_width);
}
}
// Bias toward using colors in the cache.
// TODO(huisu): Try other schemes to improve compression.
#define PALETTE_CACHE_BIAS_THRESH 4
static AOM_INLINE void optimize_palette_colors(uint16_t *color_cache,
int n_cache, int n_colors,
int stride, int *centroids,
int bit_depth) {
if (n_cache <= 0) return;
for (int i = 0; i < n_colors * stride; i += stride) {
int min_diff = abs(centroids[i] - (int)color_cache[0]);
int idx = 0;
for (int j = 1; j < n_cache; ++j) {
const int this_diff = abs(centroids[i] - color_cache[j]);
if (this_diff < min_diff) {
min_diff = this_diff;
idx = j;
}
}
const int min_threshold = (PALETTE_CACHE_BIAS_THRESH) << (bit_depth - 8);
if (min_diff <= min_threshold) centroids[i] = color_cache[idx];
}
}
/*!\brief Calculate the luma palette cost from a given color palette
*
* \ingroup palette_mode_search
* \callergraph
* Given the base colors as specified in centroids[], calculate the RD cost
* of palette mode.
*/
static AOM_INLINE void palette_rd_y(
const AV1_COMP *const cpi, MACROBLOCK *x, MB_MODE_INFO *mbmi,
BLOCK_SIZE bsize, int dc_mode_cost, const int *data, int *centroids, int n,
uint16_t *color_cache, int n_cache, MB_MODE_INFO *best_mbmi,
uint8_t *best_palette_color_map, int64_t *best_rd, int64_t *best_model_rd,
int *rate, int *rate_tokenonly, int64_t *distortion, int *skippable,
int *beat_best_rd, PICK_MODE_CONTEXT *ctx, uint8_t *blk_skip,
TX_TYPE *tx_type_map, int *beat_best_palette_rd) {
optimize_palette_colors(color_cache, n_cache, n, 1, centroids,
cpi->common.seq_params.bit_depth);
const int num_unique_colors = av1_remove_duplicates(centroids, n);
if (num_unique_colors < PALETTE_MIN_SIZE) {
// Too few unique colors to create a palette. And DC_PRED will work
// well for that case anyway. So skip.
return;
}
PALETTE_MODE_INFO *const pmi = &mbmi->palette_mode_info;
for (int i = 0; i < num_unique_colors; ++i) {
pmi->palette_colors[i] =
clip_pixel_highbd((int)centroids[i], cpi->common.seq_params.bit_depth);
}
pmi->palette_size[0] = num_unique_colors;
MACROBLOCKD *const xd = &x->e_mbd;
uint8_t *const color_map = xd->plane[0].color_index_map;
int block_width, block_height, rows, cols;
av1_get_block_dimensions(bsize, 0, xd, &block_width, &block_height, &rows,
&cols);
av1_calc_indices(data, centroids, color_map, rows * cols, num_unique_colors,
1);
extend_palette_color_map(color_map, cols, rows, block_width, block_height);
const int palette_mode_cost =
intra_mode_info_cost_y(cpi, x, mbmi, bsize, dc_mode_cost);
#if CONFIG_ORIP
mbmi->angle_delta[PLANE_TYPE_Y] = 0;
#endif
if (model_intra_yrd_and_prune(cpi, x, bsize, palette_mode_cost,
best_model_rd)) {
return;
}
RD_STATS tokenonly_rd_stats;
av1_pick_uniform_tx_size_type_yrd(cpi, x, &tokenonly_rd_stats, bsize,
*best_rd);
if (tokenonly_rd_stats.rate == INT_MAX) return;
int this_rate = tokenonly_rd_stats.rate + palette_mode_cost;
int64_t this_rd = RDCOST(x->rdmult, this_rate, tokenonly_rd_stats.dist);
if (!xd->lossless[mbmi->segment_id] &&
block_signals_txsize(mbmi->sb_type[PLANE_TYPE_Y])) {
tokenonly_rd_stats.rate -= tx_size_cost(x, bsize, mbmi->tx_size);
}
// Collect mode stats for multiwinner mode processing
const int txfm_search_done = 1;
const MV_REFERENCE_FRAME refs[2] = { -1, -1 };
store_winner_mode_stats(
&cpi->common, x, mbmi, NULL, NULL, NULL, refs, DC_PRED, color_map, bsize,
this_rd, cpi->sf.winner_mode_sf.multi_winner_mode_type, txfm_search_done);
if (this_rd < *best_rd) {
*best_rd = this_rd;
// Setting beat_best_rd flag because current mode rd is better than best_rd.
// This flag need to be updated only for palette evaluation in key frames
if (beat_best_rd) *beat_best_rd = 1;
memcpy(best_palette_color_map, color_map,
block_width * block_height * sizeof(color_map[0]));
*best_mbmi = *mbmi;
memcpy(blk_skip, x->txfm_search_info.blk_skip,
sizeof(x->txfm_search_info.blk_skip[0]) * ctx->num_4x4_blk);
av1_copy_array(tx_type_map, xd->tx_type_map, ctx->num_4x4_blk);
if (rate) *rate = this_rate;
if (rate_tokenonly) *rate_tokenonly = tokenonly_rd_stats.rate;
if (distortion) *distortion = tokenonly_rd_stats.dist;
if (skippable) *skippable = tokenonly_rd_stats.skip_txfm;
if (beat_best_palette_rd) *beat_best_palette_rd = 1;
}
}
static AOM_INLINE int is_iter_over(int curr_idx, int end_idx, int step_size) {
assert(step_size != 0);
return (step_size > 0) ? curr_idx >= end_idx : curr_idx <= end_idx;
}
// Performs count-based palette search with number of colors in interval
// [start_n, end_n) with step size step_size. If step_size < 0, then end_n can
// be less than start_n. Saves the last numbers searched in last_n_searched and
// returns the best number of colors found.
static AOM_INLINE int perform_top_color_palette_search(
const AV1_COMP *const cpi, MACROBLOCK *x, MB_MODE_INFO *mbmi,
BLOCK_SIZE bsize, int dc_mode_cost, const int *data, int *top_colors,
int start_n, int end_n, int step_size, int *last_n_searched,
uint16_t *color_cache, int n_cache, MB_MODE_INFO *best_mbmi,
uint8_t *best_palette_color_map, int64_t *best_rd, int64_t *best_model_rd,
int *rate, int *rate_tokenonly, int64_t *distortion, int *skippable,
int *beat_best_rd, PICK_MODE_CONTEXT *ctx, uint8_t *best_blk_skip,
TX_TYPE *tx_type_map) {
int centroids[PALETTE_MAX_SIZE];
int n = start_n;
int top_color_winner = end_n;
/* clang-format off */
assert(IMPLIES(step_size < 0, start_n > end_n));
/* clang-format on */
assert(IMPLIES(step_size > 0, start_n < end_n));
while (!is_iter_over(n, end_n, step_size)) {
int beat_best_palette_rd = 0;
memcpy(centroids, top_colors, n * sizeof(top_colors[0]));
palette_rd_y(cpi, x, mbmi, bsize, dc_mode_cost, data, centroids, n,
color_cache, n_cache, best_mbmi, best_palette_color_map,
best_rd, best_model_rd, rate, rate_tokenonly, distortion,
skippable, beat_best_rd, ctx, best_blk_skip, tx_type_map,
&beat_best_palette_rd);
*last_n_searched = n;
if (beat_best_palette_rd) {
top_color_winner = n;
} else if (cpi->sf.intra_sf.prune_palette_search_level == 2) {
// At search level 2, we return immediately if we don't see an improvement
return top_color_winner;
}
n += step_size;
}
return top_color_winner;
}
// Performs k-means based palette search with number of colors in interval
// [start_n, end_n) with step size step_size. If step_size < 0, then end_n can
// be less than start_n. Saves the last numbers searched in last_n_searched and
// returns the best number of colors found.
static AOM_INLINE int perform_k_means_palette_search(
const AV1_COMP *const cpi, MACROBLOCK *x, MB_MODE_INFO *mbmi,
BLOCK_SIZE bsize, int dc_mode_cost, const int *data, int lb, int ub,
int start_n, int end_n, int step_size, int *last_n_searched,
uint16_t *color_cache, int n_cache, MB_MODE_INFO *best_mbmi,
uint8_t *best_palette_color_map, int64_t *best_rd, int64_t *best_model_rd,
int *rate, int *rate_tokenonly, int64_t *distortion, int *skippable,
int *beat_best_rd, PICK_MODE_CONTEXT *ctx, uint8_t *best_blk_skip,
TX_TYPE *tx_type_map, uint8_t *color_map, int data_points) {
int centroids[PALETTE_MAX_SIZE];
const int max_itr = 50;
int n = start_n;
int top_color_winner = end_n;
/* clang-format off */
assert(IMPLIES(step_size < 0, start_n > end_n));
/* clang-format on */
assert(IMPLIES(step_size > 0, start_n < end_n));
while (!is_iter_over(n, end_n, step_size)) {
int beat_best_palette_rd = 0;
for (int i = 0; i < n; ++i) {
centroids[i] = lb + (2 * i + 1) * (ub - lb) / n / 2;
}
av1_k_means(data, centroids, color_map, data_points, n, 1, max_itr);
palette_rd_y(cpi, x, mbmi, bsize, dc_mode_cost, data, centroids, n,
color_cache, n_cache, best_mbmi, best_palette_color_map,
best_rd, best_model_rd, rate, rate_tokenonly, distortion,
skippable, beat_best_rd, ctx, best_blk_skip, tx_type_map,
&beat_best_palette_rd);
*last_n_searched = n;
if (beat_best_palette_rd) {
top_color_winner = n;
} else if (cpi->sf.intra_sf.prune_palette_search_level == 2) {
// At search level 2, we return immediately if we don't see an improvement
return top_color_winner;
}
n += step_size;
}
return top_color_winner;
}
// Sets the parameters to search the current number of colors +- 1
static AOM_INLINE void set_stage2_params(int *min_n, int *max_n, int *step_size,
int winner, int end_n) {
// Set min to winner - 1 unless we are already at the border, then we set it
// to winner + 1
*min_n = (winner == PALETTE_MIN_SIZE) ? (PALETTE_MIN_SIZE + 1)
: AOMMAX(winner - 1, PALETTE_MIN_SIZE);
// Set max to winner + 1 unless we are already at the border, then we set it
// to winner - 1
*max_n =
(winner == end_n) ? (winner - 1) : AOMMIN(winner + 1, PALETTE_MAX_SIZE);
// Set the step size to max_n - min_n so we only search those two values.
// If max_n == min_n, then set step_size to 1 to avoid infinite loop later.
*step_size = AOMMAX(1, *max_n - *min_n);
}
void av1_rd_pick_palette_intra_sby(
const AV1_COMP *cpi, MACROBLOCK *x, BLOCK_SIZE bsize, int dc_mode_cost,
MB_MODE_INFO *best_mbmi, uint8_t *best_palette_color_map, int64_t *best_rd,
int64_t *best_model_rd, int *rate, int *rate_tokenonly, int64_t *distortion,
int *skippable, int *beat_best_rd, PICK_MODE_CONTEXT *ctx,
uint8_t *best_blk_skip, TX_TYPE *tx_type_map) {
MACROBLOCKD *const xd = &x->e_mbd;
MB_MODE_INFO *const mbmi = xd->mi[0];
mbmi->mrl_index = 0;
mbmi->fsc_mode[xd->tree_type == CHROMA_PART] = 0;
assert(!is_inter_block(mbmi, xd->tree_type));
assert(av1_allow_palette(cpi->common.features.allow_screen_content_tools,
bsize));
assert(PALETTE_MAX_SIZE == 8);
assert(PALETTE_MIN_SIZE == 2);
mbmi->angle_delta[PLANE_TYPE_Y] = 0;
const int src_stride = x->plane[0].src.stride;
uint16_t *src = x->plane[0].src.buf;
int block_width, block_height, rows, cols;
av1_get_block_dimensions(bsize, 0, xd, &block_width, &block_height, &rows,
&cols);
const SequenceHeader *const seq_params = &cpi->common.seq_params;
const int bit_depth = seq_params->bit_depth;
int unused;
int count_buf[1 << 12]; // Maximum (1 << 12) color levels.
int count_buf_8bit[1 << 8]; // Maximum (1 << 8) bins for hbd path.
int colors, colors_threshold = 0;
av1_count_colors_highbd(src, src_stride, rows, cols, bit_depth, count_buf,
count_buf_8bit, &colors_threshold, &colors);
uint8_t *const color_map = xd->plane[0].color_index_map;
if (colors_threshold > 1 && colors_threshold <= 64) {
int *const data = x->palette_buffer->kmeans_data_buf;
int centroids[PALETTE_MAX_SIZE];
int lb, ub;
int *data_pt = data;
lb = ub = src[0];
for (int r = 0; r < rows; ++r) {
for (int c = 0; c < cols; ++c) {
const int val = src[c];
data_pt[c] = val;
lb = AOMMIN(lb, val);
ub = AOMMAX(ub, val);
}
src += src_stride;
data_pt += cols;
}
mbmi->mode = DC_PRED;
#if CONFIG_AIMC
mbmi->joint_y_mode_delta_angle = DC_PRED;
mbmi->y_mode_idx = DC_PRED;
#endif // CONFIG_AIMC
mbmi->filter_intra_mode_info.use_filter_intra = 0;
uint16_t color_cache[2 * PALETTE_MAX_SIZE];
const int n_cache = av1_get_palette_cache(xd, 0, color_cache);
// Find the dominant colors, stored in top_colors[].
int top_colors[PALETTE_MAX_SIZE] = { 0 };
for (int i = 0; i < AOMMIN(colors, PALETTE_MAX_SIZE); ++i) {
int max_count = 0;
for (int j = 0; j < (1 << bit_depth); ++j) {
if (count_buf[j] > max_count) {
max_count = count_buf[j];
top_colors[i] = j;
}
}
assert(max_count > 0);
count_buf[top_colors[i]] = 0;
}
// TODO(huisu@google.com): Try to avoid duplicate computation in cases
// where the dominant colors and the k-means results are similar.
if ((cpi->sf.intra_sf.prune_palette_search_level == 1) &&
(colors > PALETTE_MIN_SIZE)) {
// Start index and step size below are chosen to evaluate unique
// candidates in neighbor search, in case a winner candidate is found in
// coarse search. Example,
// 1) 8 colors (end_n = 8): 2,3,4,5,6,7,8. start_n is chosen as 2 and step
// size is chosen as 3. Therefore, coarse search will evaluate 2, 5 and 8.
// If winner is found at 5, then 4 and 6 are evaluated. Similarly, for 2
// (3) and 8 (7).
// 2) 7 colors (end_n = 7): 2,3,4,5,6,7. If start_n is chosen as 2 (same
// as for 8 colors) then step size should also be 2, to cover all
// candidates. Coarse search will evaluate 2, 4 and 6. If winner is either
// 2 or 4, 3 will be evaluated. Instead, if start_n=3 and step_size=3,
// coarse search will evaluate 3 and 6. For the winner, unique neighbors
// (3: 2,4 or 6: 5,7) would be evaluated.
// Start index for coarse palette search for dominant colors and k-means
const uint8_t start_n_lookup_table[PALETTE_MAX_SIZE + 1] = { 0, 0, 0,
3, 3, 2,
3, 3, 2 };
// Step size for coarse palette search for dominant colors and k-means
const uint8_t step_size_lookup_table[PALETTE_MAX_SIZE + 1] = { 0, 0, 0,
3, 3, 3,
3, 3, 3 };
// Choose the start index and step size for coarse search based on number
// of colors
const int max_n = AOMMIN(colors, PALETTE_MAX_SIZE);
const int min_n = start_n_lookup_table[max_n];
const int step_size = step_size_lookup_table[max_n];
assert(min_n >= PALETTE_MIN_SIZE);
// Perform top color coarse palette search to find the winner candidate
const int top_color_winner = perform_top_color_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, min_n, max_n + 1,
step_size, &unused, color_cache, n_cache, best_mbmi,
best_palette_color_map, best_rd, best_model_rd, rate, rate_tokenonly,
distortion, skippable, beat_best_rd, ctx, best_blk_skip, tx_type_map);
// Evaluate neighbors for the winner color (if winner is found) in the
// above coarse search for dominant colors
if (top_color_winner <= max_n) {
int stage2_min_n, stage2_max_n, stage2_step_size;
set_stage2_params(&stage2_min_n, &stage2_max_n, &stage2_step_size,
top_color_winner, max_n);
// perform finer search for the winner candidate
perform_top_color_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, stage2_min_n,
stage2_max_n + 1, stage2_step_size, &unused, color_cache, n_cache,
best_mbmi, best_palette_color_map, best_rd, best_model_rd, rate,
rate_tokenonly, distortion, skippable, beat_best_rd, ctx,
best_blk_skip, tx_type_map);
}
// K-means clustering.
// Perform k-means coarse palette search to find the winner candidate
const int k_means_winner = perform_k_means_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, lb, ub, min_n, max_n + 1,
step_size, &unused, color_cache, n_cache, best_mbmi,
best_palette_color_map, best_rd, best_model_rd, rate, rate_tokenonly,
distortion, skippable, beat_best_rd, ctx, best_blk_skip, tx_type_map,
color_map, rows * cols);
// Evaluate neighbors for the winner color (if winner is found) in the
// above coarse search for k-means
if (k_means_winner <= max_n) {
int start_n_stage2, end_n_stage2, step_size_stage2;
set_stage2_params(&start_n_stage2, &end_n_stage2, &step_size_stage2,
k_means_winner, max_n);
// perform finer search for the winner candidate
perform_k_means_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, lb, ub, start_n_stage2,
end_n_stage2 + 1, step_size_stage2, &unused, color_cache, n_cache,
best_mbmi, best_palette_color_map, best_rd, best_model_rd, rate,
rate_tokenonly, distortion, skippable, beat_best_rd, ctx,
best_blk_skip, tx_type_map, color_map, rows * cols);
}
} else {
const int max_n = AOMMIN(colors, PALETTE_MAX_SIZE),
min_n = PALETTE_MIN_SIZE;
// Perform top color palette search in descending order
int last_n_searched = max_n;
perform_top_color_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, max_n, min_n - 1,
-1, &last_n_searched, color_cache, n_cache, best_mbmi,
best_palette_color_map, best_rd, best_model_rd, rate, rate_tokenonly,
distortion, skippable, beat_best_rd, ctx, best_blk_skip, tx_type_map);
if (last_n_searched > min_n) {
// Search in ascending order until we get to the previous best
perform_top_color_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, top_colors, min_n,
last_n_searched, 1, &unused, color_cache, n_cache, best_mbmi,
best_palette_color_map, best_rd, best_model_rd, rate,
rate_tokenonly, distortion, skippable, beat_best_rd, ctx,
best_blk_skip, tx_type_map);
}
// K-means clustering.
if (colors == PALETTE_MIN_SIZE) {
// Special case: These colors automatically become the centroids.
assert(colors == 2);
centroids[0] = lb;
centroids[1] = ub;
palette_rd_y(cpi, x, mbmi, bsize, dc_mode_cost, data, centroids, colors,
color_cache, n_cache, best_mbmi, best_palette_color_map,
best_rd, best_model_rd, rate, rate_tokenonly, distortion,
skippable, beat_best_rd, ctx, best_blk_skip, tx_type_map,
NULL);
} else {
// Perform k-means palette search in descending order
last_n_searched = max_n;
perform_k_means_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, lb, ub, max_n, min_n - 1,
-1, &last_n_searched, color_cache, n_cache, best_mbmi,
best_palette_color_map, best_rd, best_model_rd, rate,
rate_tokenonly, distortion, skippable, beat_best_rd, ctx,
best_blk_skip, tx_type_map, color_map, rows * cols);
if (last_n_searched > min_n) {
// Search in ascending order until we get to the previous best
perform_k_means_palette_search(
cpi, x, mbmi, bsize, dc_mode_cost, data, lb, ub, min_n,
last_n_searched, 1, &unused, color_cache, n_cache, best_mbmi,
best_palette_color_map, best_rd, best_model_rd, rate,
rate_tokenonly, distortion, skippable, beat_best_rd, ctx,
best_blk_skip, tx_type_map, color_map, rows * cols);
}
}
}
}
if (best_mbmi->palette_mode_info.palette_size[0] > 0) {
memcpy(color_map, best_palette_color_map,
block_width * block_height * sizeof(best_palette_color_map[0]));
}
*mbmi = *best_mbmi;
}
void av1_rd_pick_palette_intra_sbuv(const AV1_COMP *cpi, MACROBLOCK *x,
int dc_mode_cost,
uint8_t *best_palette_color_map,
MB_MODE_INFO *const best_mbmi,
int64_t *best_rd, int *rate,
int *rate_tokenonly, int64_t *distortion,
int *skippable) {
MACROBLOCKD *const xd = &x->e_mbd;
MB_MODE_INFO *const mbmi = xd->mi[0];
assert(!is_inter_block(mbmi, xd->tree_type));
assert(xd->tree_type != LUMA_PART);
assert(av1_allow_palette(cpi->common.features.allow_screen_content_tools,
mbmi->sb_type[PLANE_TYPE_UV]));
PALETTE_MODE_INFO *const pmi = &mbmi->palette_mode_info;
const BLOCK_SIZE bsize = mbmi->sb_type[PLANE_TYPE_UV];
mbmi->fsc_mode[PLANE_TYPE_UV] = 0;
const SequenceHeader *const seq_params = &cpi->common.seq_params;
int this_rate;
int64_t this_rd;
int colors_u, colors_v, colors;
int colors_threshold_u = 0, colors_threshold_v = 0, colors_threshold = 0;
const int src_stride = x->plane[1].src.stride;
const uint16_t *const src_u = x->plane[1].src.buf;
const uint16_t *const src_v = x->plane[2].src.buf;
uint8_t *const color_map = xd->plane[1].color_index_map;
RD_STATS tokenonly_rd_stats;
int plane_block_width, plane_block_height, rows, cols;
av1_get_block_dimensions(bsize, 1, xd, &plane_block_width,
&plane_block_height, &rows, &cols);
mbmi->uv_mode = UV_DC_PRED;
#if CONFIG_AIMC
if (av1_is_directional_mode(mbmi->mode))
mbmi->uv_mode_idx = 1;
else
mbmi->uv_mode_idx = 0;
dc_mode_cost = get_uv_mode_cost(mbmi, x->mode_costs, is_cfl_allowed(xd),
mbmi->uv_mode_idx);
assert(mbmi->uv_mode_idx >= 0 && mbmi->uv_mode_idx < UV_INTRA_MODES);
#endif // CONFIG_AIMC
int count_buf[1 << 12]; // Maximum (1 << 12) color levels.
int count_buf_8bit[1 << 8]; // Maximum (1 << 8) bins for hbd path.
av1_count_colors_highbd(src_u, src_stride, rows, cols, seq_params->bit_depth,
count_buf, count_buf_8bit, &colors_threshold_u,
&colors_u);
av1_count_colors_highbd(src_v, src_stride, rows, cols, seq_params->bit_depth,
count_buf, count_buf_8bit, &colors_threshold_v,
&colors_v);
uint16_t color_cache[2 * PALETTE_MAX_SIZE];
const int n_cache = av1_get_palette_cache(xd, 1, color_cache);
colors = colors_u > colors_v ? colors_u : colors_v;
colors_threshold = colors_threshold_u > colors_threshold_v
? colors_threshold_u
: colors_threshold_v;
if (colors_threshold > 1 && colors_threshold <= 64) {
int r, c, n, i, j;
const int max_itr = 50;
int lb_u, ub_u, val_u;
int lb_v, ub_v, val_v;
int *const data = x->palette_buffer->kmeans_data_buf;
int centroids[2 * PALETTE_MAX_SIZE];
lb_u = src_u[0];
ub_u = src_u[0];
lb_v = src_v[0];
ub_v = src_v[0];
for (r = 0; r < rows; ++r) {
for (c = 0; c < cols; ++c) {
val_u = src_u[r * src_stride + c];
val_v = src_v[r * src_stride + c];
data[(r * cols + c) * 2] = val_u;
data[(r * cols + c) * 2 + 1] = val_v;
if (val_u < lb_u)
lb_u = val_u;
else if (val_u > ub_u)
ub_u = val_u;
if (val_v < lb_v)
lb_v = val_v;
else if (val_v > ub_v)
ub_v = val_v;
}
}
for (n = colors > PALETTE_MAX_SIZE ? PALETTE_MAX_SIZE : colors; n >= 2;
--n) {
for (i = 0; i < n; ++i) {
centroids[i * 2] = lb_u + (2 * i + 1) * (ub_u - lb_u) / n / 2;
centroids[i * 2 + 1] = lb_v + (2 * i + 1) * (ub_v - lb_v) / n / 2;
}
av1_k_means(data, centroids, color_map, rows * cols, n, 2, max_itr);
optimize_palette_colors(color_cache, n_cache, n, 2, centroids,
cpi->common.seq_params.bit_depth);
// Sort the U channel colors in ascending order.
for (i = 0; i < 2 * (n - 1); i += 2) {
int min_idx = i;
int min_val = centroids[i];
for (j = i + 2; j < 2 * n; j += 2)
if (centroids[j] < min_val) min_val = centroids[j], min_idx = j;
if (min_idx != i) {
int temp_u = centroids[i], temp_v = centroids[i + 1];
centroids[i] = centroids[min_idx];
centroids[i + 1] = centroids[min_idx + 1];
centroids[min_idx] = temp_u, centroids[min_idx + 1] = temp_v;
}
}
av1_calc_indices(data, centroids, color_map, rows * cols, n, 2);
extend_palette_color_map(color_map, cols, rows, plane_block_width,
plane_block_height);
pmi->palette_size[1] = n;
for (i = 1; i < 3; ++i) {
for (j = 0; j < n; ++j) {
pmi->palette_colors[i * PALETTE_MAX_SIZE + j] = clip_pixel_highbd(
(int)centroids[j * 2 + i - 1], seq_params->bit_depth);
}
}
av1_txfm_uvrd(cpi, x, &tokenonly_rd_stats, *best_rd);
if (tokenonly_rd_stats.rate == INT_MAX) continue;
this_rate = tokenonly_rd_stats.rate +
intra_mode_info_cost_uv(cpi, x, mbmi, bsize, dc_mode_cost);
this_rd = RDCOST(x->rdmult, this_rate, tokenonly_rd_stats.dist);
if (this_rd < *best_rd) {
*best_rd = this_rd;
*best_mbmi = *mbmi;
memcpy(best_palette_color_map, color_map,
plane_block_width * plane_block_height *
sizeof(best_palette_color_map[0]));
*rate = this_rate;
*distortion = tokenonly_rd_stats.dist;
*rate_tokenonly = tokenonly_rd_stats.rate;
*skippable = tokenonly_rd_stats.skip_txfm;
}
}
}
if (best_mbmi->palette_mode_info.palette_size[1] > 0) {
memcpy(color_map, best_palette_color_map,
plane_block_width * plane_block_height *
sizeof(best_palette_color_map[0]));
}
}
void av1_restore_uv_color_map(const AV1_COMP *cpi, MACROBLOCK *x) {
MACROBLOCKD *const xd = &x->e_mbd;
MB_MODE_INFO *const mbmi = xd->mi[0];
PALETTE_MODE_INFO *const pmi = &mbmi->palette_mode_info;
assert(xd->tree_type != LUMA_PART);
const BLOCK_SIZE bsize = mbmi->sb_type[PLANE_TYPE_UV];
int src_stride = x->plane[1].src.stride;
const uint16_t *const src_u = x->plane[1].src.buf;
const uint16_t *const src_v = x->plane[2].src.buf;
int *const data = x->palette_buffer->kmeans_data_buf;
int centroids[2 * PALETTE_MAX_SIZE];
uint8_t *const color_map = xd->plane[1].color_index_map;
int r, c;
int plane_block_width, plane_block_height, rows, cols;
(void)cpi;
av1_get_block_dimensions(bsize, 1, xd, &plane_block_width,
&plane_block_height, &rows, &cols);
for (r = 0; r < rows; ++r) {
for (c = 0; c < cols; ++c) {
data[(r * cols + c) * 2] = src_u[r * src_stride + c];
data[(r * cols + c) * 2 + 1] = src_v[r * src_stride + c];
}
}
for (r = 1; r < 3; ++r) {
for (c = 0; c < pmi->palette_size[1]; ++c) {
centroids[c * 2 + r - 1] = pmi->palette_colors[r * PALETTE_MAX_SIZE + c];
}
}
av1_calc_indices(data, centroids, color_map, rows * cols,
pmi->palette_size[1], 2);
extend_palette_color_map(color_map, cols, rows, plane_block_width,
plane_block_height);
}