|  | /* | 
|  | * Copyright (c) 2016, Alliance for Open Media. All rights reserved. | 
|  | * | 
|  | * This source code is subject to the terms of the BSD 2 Clause License and | 
|  | * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License | 
|  | * was not distributed with this source code in the LICENSE file, you can | 
|  | * obtain it at www.aomedia.org/license/software. 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 www.aomedia.org/license/patent. | 
|  | */ | 
|  |  | 
|  | #include <assert.h> | 
|  | #include <stdint.h> | 
|  | #include <stdlib.h> | 
|  | #include <string.h> | 
|  |  | 
|  | #include "av1/common/blockd.h" | 
|  | #include "av1/encoder/palette.h" | 
|  | #include "av1/encoder/random.h" | 
|  |  | 
|  | #ifndef AV1_K_MEANS_DIM | 
|  | #error "This template requires AV1_K_MEANS_DIM to be defined" | 
|  | #endif | 
|  |  | 
|  | #define RENAME_(x, y) AV1_K_MEANS_RENAME(x, y) | 
|  | #define RENAME(x) RENAME_(x, AV1_K_MEANS_DIM) | 
|  | #define K_MEANS_RENAME_C(x, y) x##_dim##y##_c | 
|  | #define RENAME_C_(x, y) K_MEANS_RENAME_C(x, y) | 
|  | #define RENAME_C(x) RENAME_C_(x, AV1_K_MEANS_DIM) | 
|  |  | 
|  | // Though we want to compute the smallest L2 norm, in 1 dimension, | 
|  | // it is equivalent to find the smallest L1 norm and then square it. | 
|  | // This is preferrable for speed, especially on the SIMD side. | 
|  | static int RENAME(calc_dist)(const int16_t *p1, const int16_t *p2) { | 
|  | #if AV1_K_MEANS_DIM == 1 | 
|  | return abs(p1[0] - p2[0]); | 
|  | #else | 
|  | int dist = 0; | 
|  | for (int i = 0; i < AV1_K_MEANS_DIM; ++i) { | 
|  | const int diff = p1[i] - p2[i]; | 
|  | dist += diff * diff; | 
|  | } | 
|  | return dist; | 
|  | #endif | 
|  | } | 
|  |  | 
|  | void RENAME_C(av1_calc_indices)(const int16_t *data, const int16_t *centroids, | 
|  | uint8_t *indices, int64_t *dist, int n, int k) { | 
|  | if (dist) { | 
|  | *dist = 0; | 
|  | } | 
|  | for (int i = 0; i < n; ++i) { | 
|  | int min_dist = RENAME(calc_dist)(data + i * AV1_K_MEANS_DIM, centroids); | 
|  | indices[i] = 0; | 
|  | for (int j = 1; j < k; ++j) { | 
|  | const int this_dist = RENAME(calc_dist)(data + i * AV1_K_MEANS_DIM, | 
|  | centroids + j * AV1_K_MEANS_DIM); | 
|  | if (this_dist < min_dist) { | 
|  | min_dist = this_dist; | 
|  | indices[i] = j; | 
|  | } | 
|  | } | 
|  | if (dist) { | 
|  | #if AV1_K_MEANS_DIM == 1 | 
|  | *dist += min_dist * min_dist; | 
|  | #else | 
|  | *dist += min_dist; | 
|  | #endif | 
|  | } | 
|  | } | 
|  | } | 
|  |  | 
|  | static void RENAME(calc_centroids)(const int16_t *data, int16_t *centroids, | 
|  | const uint8_t *indices, int n, int k) { | 
|  | int i, j; | 
|  | int count[PALETTE_MAX_SIZE] = { 0 }; | 
|  | int centroids_sum[AV1_K_MEANS_DIM * PALETTE_MAX_SIZE]; | 
|  | unsigned int rand_state = (unsigned int)data[0]; | 
|  | assert(n <= 32768); | 
|  | memset(centroids_sum, 0, sizeof(centroids_sum[0]) * k * AV1_K_MEANS_DIM); | 
|  |  | 
|  | for (i = 0; i < n; ++i) { | 
|  | const int index = indices[i]; | 
|  | assert(index < k); | 
|  | ++count[index]; | 
|  | for (j = 0; j < AV1_K_MEANS_DIM; ++j) { | 
|  | centroids_sum[index * AV1_K_MEANS_DIM + j] += | 
|  | data[i * AV1_K_MEANS_DIM + j]; | 
|  | } | 
|  | } | 
|  |  | 
|  | for (i = 0; i < k; ++i) { | 
|  | if (count[i] == 0) { | 
|  | memcpy(centroids + i * AV1_K_MEANS_DIM, | 
|  | data + (lcg_rand16(&rand_state) % n) * AV1_K_MEANS_DIM, | 
|  | sizeof(centroids[0]) * AV1_K_MEANS_DIM); | 
|  | } else { | 
|  | for (j = 0; j < AV1_K_MEANS_DIM; ++j) { | 
|  | centroids[i * AV1_K_MEANS_DIM + j] = | 
|  | DIVIDE_AND_ROUND(centroids_sum[i * AV1_K_MEANS_DIM + j], count[i]); | 
|  | } | 
|  | } | 
|  | } | 
|  | } | 
|  |  | 
|  | void RENAME(av1_k_means)(const int16_t *data, int16_t *centroids, | 
|  | uint8_t *indices, int n, int k, int max_itr) { | 
|  | int16_t centroids_tmp[AV1_K_MEANS_DIM * PALETTE_MAX_SIZE]; | 
|  | uint8_t indices_tmp[MAX_PALETTE_BLOCK_WIDTH * MAX_PALETTE_BLOCK_HEIGHT]; | 
|  | int16_t *meta_centroids[2] = { centroids, centroids_tmp }; | 
|  | uint8_t *meta_indices[2] = { indices, indices_tmp }; | 
|  | int i, l = 0, prev_l, best_l = 0; | 
|  | int64_t this_dist; | 
|  |  | 
|  | assert(n <= MAX_PALETTE_BLOCK_WIDTH * MAX_PALETTE_BLOCK_HEIGHT); | 
|  |  | 
|  | #if AV1_K_MEANS_DIM == 1 | 
|  | av1_calc_indices_dim1(data, centroids, indices, &this_dist, n, k); | 
|  | #else | 
|  | av1_calc_indices_dim2(data, centroids, indices, &this_dist, n, k); | 
|  | #endif | 
|  |  | 
|  | for (i = 0; i < max_itr; ++i) { | 
|  | const int64_t prev_dist = this_dist; | 
|  | prev_l = l; | 
|  | l = (l == 1) ? 0 : 1; | 
|  |  | 
|  | RENAME(calc_centroids)(data, meta_centroids[l], meta_indices[prev_l], n, k); | 
|  | if (!memcmp(meta_centroids[l], meta_centroids[prev_l], | 
|  | sizeof(centroids[0]) * k * AV1_K_MEANS_DIM)) { | 
|  | break; | 
|  | } | 
|  | #if AV1_K_MEANS_DIM == 1 | 
|  | av1_calc_indices_dim1(data, meta_centroids[l], meta_indices[l], &this_dist, | 
|  | n, k); | 
|  | #else | 
|  | av1_calc_indices_dim2(data, meta_centroids[l], meta_indices[l], &this_dist, | 
|  | n, k); | 
|  | #endif | 
|  |  | 
|  | if (this_dist > prev_dist) { | 
|  | best_l = prev_l; | 
|  | break; | 
|  | } | 
|  | } | 
|  | if (i == max_itr) best_l = l; | 
|  | if (best_l != 0) { | 
|  | memcpy(centroids, meta_centroids[1], | 
|  | sizeof(centroids[0]) * k * AV1_K_MEANS_DIM); | 
|  | memcpy(indices, meta_indices[1], sizeof(indices[0]) * n); | 
|  | } | 
|  | } | 
|  | #undef RENAME_ | 
|  | #undef RENAME | 
|  | #undef K_MEANS_RENAME_C | 
|  | #undef RENAME_C_ | 
|  | #undef RENAME_C |