| /* |
| * 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)); |
| #ifndef NDEBUG |
| int n_in_cache = 0; |
| #endif // NDEBUG |
| int in_cache_flags[PALETTE_MAX_SIZE]; |
| memset(in_cache_flags, 0, sizeof(in_cache_flags)); |
| #if CONFIG_PALETTE_IMPROVEMENTS |
| 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_PALETTE_IMPROVEMENTS |
| for (int j = 0; j < n_colors; ++j) { |
| if (colors[j] == color_cache[i]) { |
| in_cache_flags[j] = 1; |
| cache_color_found[i] = 1; |
| #ifndef NDEBUG |
| ++n_in_cache; |
| #endif // NDEBUG |
| 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); |
| mbmi->angle_delta[PLANE_TYPE_Y] = 0; |
| 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])); |
| #if CONFIG_SCC_DETERMINATION |
| // Gather the stats to determine whether to use screen content tools in |
| // function av1_determine_sc_tools_with_encoding(). |
| x->palette_pixels += (block_width * block_height); |
| #endif // CONFIG_SCC_DETERMINATION |
| } |
| *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, |
| #if CONFIG_UV_CFL |
| xd, |
| #endif // CONFIG_UV_CFL |
| 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); |
| } |
