/***************************************************************************** * This file is part of uvg266 VVC encoder. * * Copyright (c) 2021, Tampere University, ITU/ISO/IEC, project contributors * All rights reserved. * * Redistribution and use in source and binary forms, with or without modification, * are permitted provided that the following conditions are met: * * * Redistributions of source code must retain the above copyright notice, this * list of conditions and the following disclaimer. * * * Redistributions in binary form must reproduce the above copyright notice, this * list of conditions and the following disclaimer in the documentation and/or * other materials provided with the distribution. * * * Neither the name of the Tampere University or ITU/ISO/IEC nor the names of its * contributors may be used to endorse or promote products derived from * this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION HOWEVER CAUSED AND ON * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * INCLUDING NEGLIGENCE OR OTHERWISE ARISING IN ANY WAY OUT OF THE USE OF THIS ****************************************************************************/ #include "ml_intra_cu_depth_pred.h" static int tree_predict_merge_depth_1(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->merge_variance <= 140.3129) { if (p_features->var_of_sub_var <= 569.6553) { if (p_features->merge_variance <= 20.8854) { *p_nb_iter = 19428.0; *p_nb_bad = 1740.0; return -1.0000; } else if (p_features->sub_variance_0 <= 9.1015) { if (p_features->merge_variance <= 39.132) { *p_nb_iter = 1166.0; *p_nb_bad = 358.0; return -1.0000; } else { *p_nb_iter = 1049.0; *p_nb_bad = 392.0; return 1.0000; } } else { *p_nb_iter = 9371.0; *p_nb_bad = 1805.0; return -1.0000; } } else if (p_features->sub_variance_2 <= 23.3193) { *p_nb_iter = 1059.0; *p_nb_bad = 329.0; return 1.0000; } else if (p_features->sub_variance_1 <= 30.7348) { *p_nb_iter = 1042.0; *p_nb_bad = 395.0; return 1.0000; } else { *p_nb_iter = 1756.0; *p_nb_bad = 588.0; return -1.0000; } } else if (p_features->merge_variance <= 857.8047) { if (p_features->var_of_sub_var <= 66593.5553) { if (p_features->sub_variance_0 <= 12.1697) { *p_nb_iter = 2006.0; *p_nb_bad = 374.0; return 1.0000; } else if (p_features->neigh_variance_C <= 646.8204) { if (p_features->neigh_variance_A <= 664.7609) { if (p_features->neigh_variance_B <= 571.2004) { if (p_features->var_of_sub_mean <= 4.1069) { *p_nb_iter = 1208.0; *p_nb_bad = 399.0; return 1.0000; } else if (p_features->var_of_sub_var <= 11832.6635) { *p_nb_iter = 8701.0; *p_nb_bad = 3037.0; return -1.0000; } else if (p_features->neigh_variance_A <= 142.298) { *p_nb_iter = 1025.0; *p_nb_bad = 290.0; return 1.0000; } else if (p_features->variance <= 394.4839) { *p_nb_iter = 1156.0; *p_nb_bad = 489.0; return 1.0000; } else { *p_nb_iter = 1150.0; *p_nb_bad = 503.0; return -1.0000; } } else { *p_nb_iter = 1777.0; *p_nb_bad = 558.0; return 1.0000; } } else { *p_nb_iter = 1587.0; *p_nb_bad = 411.0; return 1.0000; } } else { *p_nb_iter = 1980.0; *p_nb_bad = 474.0; return 1.0000; } } else { *p_nb_iter = 3613.0; *p_nb_bad = 475.0; return 1.0000; } } else { *p_nb_iter = 20926.0; *p_nb_bad = 1873.0; return 1.0000; } } static int tree_predict_merge_depth_2(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->merge_variance <= 119.4611) { if (p_features->var_of_sub_var <= 1078.0638) { if (p_features->neigh_variance_B <= 70.2189) { *p_nb_iter = 29253.0; *p_nb_bad = 3837.0; return -1.0000; } else if (p_features->variance <= 20.8711) { *p_nb_iter = 1292.0; *p_nb_bad = 458.0; return 2.0000; } else { *p_nb_iter = 1707.0; *p_nb_bad = 399.0; return -1.0000; } } else if (p_features->var_of_sub_var <= 3300.4034) { *p_nb_iter = 1554.0; *p_nb_bad = 675.0; return -1.0000; } else { *p_nb_iter = 1540.0; *p_nb_bad = 429.0; return 2.0000; } } else if (p_features->merge_variance <= 696.1989) { if (p_features->var_of_sub_var <= 31803.3242) { if (p_features->sub_variance_2 <= 10.3845) { *p_nb_iter = 3473.0; *p_nb_bad = 768.0; return 2.0000; } else if (p_features->neigh_variance_C <= 571.5329) { if (p_features->neigh_variance_B <= 492.8159) { if (p_features->neigh_variance_B <= 38.9672) { *p_nb_iter = 1887.0; *p_nb_bad = 588.0; return 2.0000; } else if (p_features->neigh_variance_A <= 380.5927) { if (p_features->sub_variance_1 <= 19.9678) { *p_nb_iter = 1686.0; *p_nb_bad = 721.0; return 2.0000; } else if (p_features->neigh_variance_A <= 66.6749) { *p_nb_iter = 1440.0; *p_nb_bad = 631.0; return 2.0000; } else { *p_nb_iter = 5772.0; *p_nb_bad = 2031.0; return -1.0000; } } else { *p_nb_iter = 1791.0; *p_nb_bad = 619.0; return 2.0000; } } else { *p_nb_iter = 1624.0; *p_nb_bad = 494.0; return 2.0000; } } else { *p_nb_iter = 1298.0; *p_nb_bad = 312.0; return 2.0000; } } else { *p_nb_iter = 4577.0; *p_nb_bad = 892.0; return 2.0000; } } else { *p_nb_iter = 21106.0; *p_nb_bad = 2744.0; return 2.0000; } } static int tree_predict_merge_depth_3(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->merge_variance <= 80.1487) { if (p_features->neigh_variance_C <= 83.7148) { *p_nb_iter = 29806.0; *p_nb_bad = 3603.0; return -1.0000; } else { *p_nb_iter = 1003.0; *p_nb_bad = 421.0; return 3.0000; } } else if (p_features->merge_variance <= 351.8138) { if (p_features->neigh_variance_C <= 255.4236) { if (p_features->neigh_variance_B <= 260.5349) { if (p_features->var_of_sub_var <= 6381.513) { if (p_features->neigh_variance_A <= 244.2556) { if (p_features->sub_variance_0 <= 4.75) { *p_nb_iter = 1290.0; *p_nb_bad = 525.0; return 3.0000; } else if (p_features->neigh_variance_B <= 16.9287) { *p_nb_iter = 1045.0; *p_nb_bad = 499.0; return 3.0000; } else { *p_nb_iter = 6901.0; *p_nb_bad = 2494.0; return -1.0000; } } else { *p_nb_iter = 1332.0; *p_nb_bad = 408.0; return 3.0000; } } else { *p_nb_iter = 2929.0; *p_nb_bad = 842.0; return 3.0000; } } else { *p_nb_iter = 2239.0; *p_nb_bad = 572.0; return 3.0000; } } else { *p_nb_iter = 2777.0; *p_nb_bad = 714.0; return 3.0000; } } else { *p_nb_iter = 30678.0; *p_nb_bad = 5409.0; return 3.0000; } } static int tree_predict_merge_depth_4(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->neigh_variance_C <= 240.2773) { if (p_features->neigh_variance_B <= 227.5898) { if (p_features->neigh_variance_A <= 195.4844) { if (p_features->variance <= 203.3086) { if (p_features->qp <= 32) { if (p_features->neigh_variance_C <= 102.2344) { if (p_features->neigh_variance_B <= 116.4961) { if (p_features->variance <= 89.4023) { *p_nb_iter = 27398.0; *p_nb_bad = 4665.0; return -1.0000; } else { *p_nb_iter = 1676.0; *p_nb_bad = 795.0; return 4.0000; } } else { *p_nb_iter = 1405.0; *p_nb_bad = 566.0; return 4.0000; } } else { *p_nb_iter = 2827.0; *p_nb_bad = 1173.0; return 4.0000; } } else { *p_nb_iter = 8871.0; *p_nb_bad = 822.0; return -1.0000; } } else { *p_nb_iter = 3162.0; *p_nb_bad = 718.0; return 4.0000; } } else { *p_nb_iter = 6154.0; *p_nb_bad = 1397.0; return 4.0000; } } else { *p_nb_iter = 9385.0; *p_nb_bad = 1609.0; return 4.0000; } } else { *p_nb_iter = 19122.0; *p_nb_bad = 2960.0; return 4.0000; } } static int tree_predict_split_depth_0(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->var_of_sub_var <= 12754.7856) { if (p_features->var_of_sub_var <= 137.9034) { *p_nb_iter = 25155.0; *p_nb_bad = 2959.0; return 0.0000; } else if (p_features->sub_variance_2 <= 13.2892) { *p_nb_iter = 1080.0; *p_nb_bad = 383.0; return -1.0000; } else if (p_features->variance <= 564.1738) { if (p_features->var_of_sub_var <= 1185.4728) { *p_nb_iter = 6067.0; *p_nb_bad = 1699.0; return 0.0000; } else if (p_features->var_of_sub_mean <= 46.2388) { if (p_features->sub_variance_0 <= 46.8708) { *p_nb_iter = 1088.0; *p_nb_bad = 377.0; return -1.0000; } else if (p_features->sub_variance_3 <= 61.4213) { *p_nb_iter = 1183.0; *p_nb_bad = 498.0; return -1.0000; } else { *p_nb_iter = 3416.0; *p_nb_bad = 1373.0; return 0.0000; } } else { *p_nb_iter = 3769.0; *p_nb_bad = 1093.0; return 0.0000; } } else { *p_nb_iter = 1036.0; *p_nb_bad = 434.0; return -1.0000; } } else if (p_features->var_of_sub_var <= 98333.8279) { if (p_features->variance <= 987.2333) { if (p_features->var_of_sub_var <= 37261.2896) { if (p_features->variance <= 238.2248) { *p_nb_iter = 1323.0; *p_nb_bad = 301.0; return -1.0000; } else if (p_features->var_of_sub_var <= 17347.3971) { *p_nb_iter = 1215.0; *p_nb_bad = 550.0; return 0.0000; } else if (p_features->qp <= 22) { *p_nb_iter = 1000.0; *p_nb_bad = 493.0; return 0.0000; } else { *p_nb_iter = 2640.0; *p_nb_bad = 1121.0; return -1.0000; } } else { *p_nb_iter = 5188.0; *p_nb_bad = 1248.0; return -1.0000; } } else { *p_nb_iter = 2323.0; *p_nb_bad = 274.0; return -1.0000; } } else { *p_nb_iter = 21357.0; *p_nb_bad = 1829.0; return -1.0000; } } static int tree_predict_split_depth_1(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->var_of_sub_var <= 1138.9473) { *p_nb_iter = 32445.0; *p_nb_bad = 4580.0; return 1.0000; } else if (p_features->var_of_sub_var <= 27289.2117) { if (p_features->sub_variance_1 <= 12.0603) { *p_nb_iter = 1900.0; *p_nb_bad = 401.0; return -1.0000; } else if (p_features->var_of_sub_var <= 5841.4773) { if (p_features->variance <= 72.4175) { *p_nb_iter = 1000.0; *p_nb_bad = 356.0; return -1.0000; } else if (p_features->neigh_variance_A <= 633.8163) { *p_nb_iter = 5279.0; *p_nb_bad = 1961.0; return 1.0000; } else { *p_nb_iter = 1176.0; *p_nb_bad = 527.0; return -1.0000; } } else if (p_features->sub_variance_0 <= 38.3035) { *p_nb_iter = 1251.0; *p_nb_bad = 293.0; return -1.0000; } else if (p_features->neigh_variance_B <= 664.9494) { if (p_features->sub_variance_3 <= 45.8181) { *p_nb_iter = 1276.0; *p_nb_bad = 471.0; return -1.0000; } else if (p_features->sub_variance_3 <= 404.3086) { if (p_features->sub_variance_1 <= 99.8715) { *p_nb_iter = 1005.0; *p_nb_bad = 435.0; return -1.0000; } else if (p_features->sub_variance_0 <= 282.3064) { *p_nb_iter = 1370.0; *p_nb_bad = 539.0; return 1.0000; } else { *p_nb_iter = 1013.0; *p_nb_bad = 495.0; return -1.0000; } } else { *p_nb_iter = 1000.0; *p_nb_bad = 379.0; return -1.0000; } } else { *p_nb_iter = 2270.0; *p_nb_bad = 679.0; return -1.0000; } } else { *p_nb_iter = 29015.0; *p_nb_bad = 3950.0; return -1.0000; } } static int tree_predict_split_depth_2(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->var_of_sub_var <= 2597.4529) { if (p_features->var_of_sub_var <= 146.7734) { *p_nb_iter = 23216.0; *p_nb_bad = 1560.0; return 2.0000; } else if (p_features->merge_variance <= 259.6952) { *p_nb_iter = 7470.0; *p_nb_bad = 1902.0; return 2.0000; } else if (p_features->qp <= 27) { if (p_features->variance <= 73.9929) { *p_nb_iter = 1138.0; *p_nb_bad = 486.0; return -1.0000; } else { *p_nb_iter = 1619.0; *p_nb_bad = 716.0; return 2.0000; } } else { *p_nb_iter = 2425.0; *p_nb_bad = 861.0; return 2.0000; } } else if (p_features->var_of_sub_var <= 60850.5208) { if (p_features->var_of_sub_var <= 10144.602) { if (p_features->neigh_variance_C <= 926.8972) { if (p_features->sub_variance_0 <= 26.6006) { *p_nb_iter = 1796.0; *p_nb_bad = 586.0; return -1.0000; } else if (p_features->neigh_variance_A <= 493.5849) { if (p_features->neigh_variance_A <= 72.9516) { *p_nb_iter = 1326.0; *p_nb_bad = 557.0; return -1.0000; } else if (p_features->variance <= 156.4014) { *p_nb_iter = 1210.0; *p_nb_bad = 563.0; return -1.0000; } else { *p_nb_iter = 1920.0; *p_nb_bad = 817.0; return 2.0000; } } else { *p_nb_iter = 1106.0; *p_nb_bad = 437.0; return -1.0000; } } else { *p_nb_iter = 1001.0; *p_nb_bad = 278.0; return -1.0000; } } else { *p_nb_iter = 13068.0; *p_nb_bad = 3612.0; return -1.0000; } } else { *p_nb_iter = 22705.0; *p_nb_bad = 2687.0; return -1.0000; } } static int tree_predict_split_depth_3(features_s* p_features, double* p_nb_iter, double* p_nb_bad) { if (p_features->var_of_sub_var <= 818.5173) { if (p_features->merge_variance <= 62.7641) { *p_nb_iter = 20568.0; *p_nb_bad = 767.0; return 3.0000; } else if (p_features->qp <= 27) { if (p_features->variance <= 9.4219) { *p_nb_iter = 1255.0; *p_nb_bad = 206.0; return 3.0000; } else if (p_features->merge_variance <= 375.2185) { *p_nb_iter = 3999.0; *p_nb_bad = 1321.0; return 3.0000; } else { *p_nb_iter = 1786.0; *p_nb_bad = 817.0; return -1.0000; } } else { *p_nb_iter = 5286.0; *p_nb_bad = 737.0; return 3.0000; } } else if (p_features->var_of_sub_var <= 37332.3018) { if (p_features->var_of_sub_var <= 7585.0282) { if (p_features->qp <= 32) { if (p_features->neigh_variance_C <= 330.2178) { if (p_features->sub_variance_0 <= 8.5273) { *p_nb_iter = 1114.0; *p_nb_bad = 346.0; return -1.0000; } else if (p_features->neigh_variance_B <= 221.5469) { if (p_features->var_of_sub_var <= 1989.7928) { *p_nb_iter = 1539.0; *p_nb_bad = 606.0; return 3.0000; } else if (p_features->variance <= 155.5974) { *p_nb_iter = 1298.0; *p_nb_bad = 634.0; return 3.0000; } else { *p_nb_iter = 1076.0; *p_nb_bad = 456.0; return -1.0000; } } else { *p_nb_iter = 1644.0; *p_nb_bad = 639.0; return -1.0000; } } else { *p_nb_iter = 2401.0; *p_nb_bad = 713.0; return -1.0000; } } else if (p_features->merge_variance <= 281.9509) { *p_nb_iter = 1020.0; *p_nb_bad = 262.0; return 3.0000; } else { *p_nb_iter = 1278.0; *p_nb_bad = 594.0; return -1.0000; } } else { *p_nb_iter = 10507.0; *p_nb_bad = 2943.0; return -1.0000; } } else { *p_nb_iter = 25229.0; *p_nb_bad = 3060.0; return -1.0000; } } /** * Allocate the structure and buffer */ ml_intra_ctu_pred_t* kvz_init_ml_intra_depth_const() { ml_intra_ctu_pred_t* ml_intra_depth_ctu = NULL; // Allocate the ml_intra_ctu_pred_t strucutre ml_intra_depth_ctu = MALLOC(ml_intra_ctu_pred_t, 1); if (!ml_intra_depth_ctu) { fprintf(stderr, "Memory allocation failed!\n"); assert(0); } // Set the number of number of deth add to 1 by default ml_intra_depth_ctu->i_nb_addDepth = 1; // Set the extra Upper Expansion in the upper_depth enabled by default ml_intra_depth_ctu->b_extra_up_exp = true; // Allocate the depth matrices ml_intra_depth_ctu->_mat_lower_depth = MALLOC(uint8_t, LCU_DEPTH_MAT_SIZE); if (!ml_intra_depth_ctu->_mat_lower_depth) { fprintf(stderr, "Memory allocation failed!\n"); assert(0); } ml_intra_depth_ctu->_mat_upper_depth = MALLOC(uint8_t, LCU_DEPTH_MAT_SIZE); if (!ml_intra_depth_ctu->_mat_upper_depth) { fprintf(stderr, "Memory allocation failed!\n"); assert(0); } return ml_intra_depth_ctu; }; /** * Fee the bufer and structure */ void kvz_end_ml_intra_depth_const(ml_intra_ctu_pred_t* ml_intra_depth_ctu) { FREE_POINTER(ml_intra_depth_ctu->_mat_lower_depth); FREE_POINTER(ml_intra_depth_ctu->_mat_upper_depth); FREE_POINTER(ml_intra_depth_ctu); } // Initialize to 0 all the features static void features_init_array(features_s* arr_features, int16_t _size, int _qp)//, int _NB_pixels) { int16_t i = 0; for (i = 0; i < _size; ++i) { arr_features[i].variance = 0.0; arr_features[i].sub_variance_0 = 0.0; arr_features[i].sub_variance_1 = 0.0; arr_features[i].sub_variance_2 = 0.0; arr_features[i].sub_variance_3 = 0.0; arr_features[i].merge_variance = 0.0; arr_features[i].neigh_variance_A = 0.0; arr_features[i].neigh_variance_B = 0.0; arr_features[i].neigh_variance_C = 0.0; arr_features[i].var_of_sub_mean = 0.0; arr_features[i].qp = _qp; //arr_features[i].NB_pixels = _NB_pixels; } } /*! * \brief Compute the average of a block inside an 8 bits 2D vector. * * \param _mat_src First depth map. * \param _x X coordinate of the start of the block inside the matrix. * \param _x_end X coordinate of the end of the block inside the matrix. * \param _y Y coordinate of the start of the block inside the matrix. * \param _y_end Y coordinate of the end of the block inside the matrix. * \param _width Width of the matrix. * \return average value of the block, -1 if error. */ static INLINE double vect_average_blck_int8(const kvz_pixel* _mat_src, size_t _x, size_t _x_end, size_t _y, size_t _y_end, size_t _width) { if (_mat_src == NULL) { fprintf(stderr, "null pointer as parameter."); assert(0); return -1.0; } double block_size = (double)(_x_end - _x) * (double)(_y_end - _y); double avg_vect = 0.0; //STD_print_matrix(_mat_src,64, 64); for (size_t i_y = _y; i_y < _y_end; ++i_y) { size_t i_y_line = i_y * _width; for (size_t i_x = _x; i_x < _x_end; ++i_x) { avg_vect = avg_vect + (double)_mat_src[i_x + i_y_line]; } } return avg_vect / (double)(block_size); } /*! * \brief Compute the variance of a block inside an 8 bits 2D vector. * * \param _mat_src First depth map. * \param _x X coordinate of the start of the block inside the matrix. * \param _x_end X coordinate of the end of the block inside the matrix. * \param _y Y coordinate of the start of the block inside the matrix. * \param _y_end Y coordinate of the end of the block inside the matrix. * \param _avg_blck Average value of the block. * \param _width Width of the matrix. * \return average value of the block, -1 if error. */ static INLINE double vect_variance_blck_int8(const kvz_pixel* _mat_src, size_t _x, size_t _x_end, size_t _y, size_t _y_end, double _avg_blck, size_t _width) { if (_mat_src == NULL) { fprintf(stderr, "null pointer as parameter."); assert(0); return -1.0; } double block_size = (double)(_x_end - _x) * (double)(_y_end - _y); double variance = 0.0; for (size_t i_y = _y; i_y < _y_end; ++i_y) { size_t i_y_line = i_y * _width; for (size_t i_x = _x; i_x < _x_end; ++i_x) { variance = variance + pow2((double)(_mat_src[i_x + i_y_line]) - _avg_blck); } } return variance / (double)(block_size); } /*! * \brief Function to compute the average and the variance of a pixel block inside of a LCU. * * \param arr_luma_px Array of the pixels of the block. * \param i_xLcu X coordinate of the lcu. * \param i_yLcu Y coordinate of the lcu. * \param i_xBlck X coordinate of the pixel block inside the LCU. * \param i_yBlck Y coordinate of the pixel block inside the LCU. * \param i_blockSize Size of the block in pixels (4, 8, 16, 32 or 64). * \param i_width Width of the frame in pixels. * \param i_height Height of the frame in pixels. * \param p_average Pointer to be filled with the average. * \param p_variance Pointer to be filled with the variance; * \return None. */ static INLINE void features_var_avg_blck(kvz_pixel* arr_luma_px, uint32_t i_xLcu, uint32_t i_yLcu, uint32_t i_xBlck, uint32_t i_yBlck, uint8_t i_blockSize, int32_t i_width, int32_t i_height, double* p_average, double* p_variance) { uint32_t iXMax = CR_XMAX(i_xLcu, i_blockSize + i_xBlck, i_width); uint32_t iYMax = CR_YMAX(i_yLcu, i_blockSize + i_yBlck, i_height); *p_average = vect_average_blck_int8(arr_luma_px, i_xBlck, iXMax, i_yBlck, iYMax, 64); *p_variance = vect_variance_blck_int8(arr_luma_px, i_xBlck, iXMax, i_yBlck, iYMax, (*p_average), 64); } /*! * \brief Function to combine the variance and mean values of four blocks. * * \param arr_var Array of 4*4 variances of the LCU. * \param arr_avgLuma Array of 4*4 mean values of the LCU. * \param i_x X coordinate of the top left block. * \param i_y Y coordinate of the top left block. * \param i_depth Depth of the blocks (0,1,2,3 or 4). * \param p_varianceC Pointer to be filled with the combined variance. * \param p_avgLumaC Pointer to be filled with the combined average. * \return None. */ static INLINE void features_combine_var(double* arr_var, double* arr_avgLuma, uint32_t i_x, uint32_t i_y, uint32_t i_depth, double* p_varianceC, double* p_avgLumaC) { double d_var_temp_1 = 0.0; double d_var_temp_2 = 0.0; double d_avg_temp_1 = 0.0; double d_avg_temp_2 = 0.0; int16_t i_subCU = (i_x + (i_y << 4)) << (4 - i_depth); int16_t i_rows = (16 << (3 - i_depth)); int16_t i_sb0 = i_subCU; /*!< Top left sub block index */ int16_t i_sb1 = i_subCU + (1 << (3 - i_depth)); /*!< Top right sub block index */ int16_t i_sb2 = i_subCU + i_rows; /*!< Bottom left sub block index */ int16_t i_sb3 = i_subCU + i_rows + (1 << (3 - i_depth)); /*!< Bottom right sub block index */ d_avg_temp_1 = (arr_avgLuma[i_sb0] + arr_avgLuma[i_sb1]) / 2.0; d_avg_temp_2 = (arr_avgLuma[i_sb2] + arr_avgLuma[i_sb3]) / 2.0; d_var_temp_1 = (2.0 * (arr_var[i_sb0] + arr_var[i_sb1]) + pow2((arr_avgLuma[i_sb0] - arr_avgLuma[i_sb1]))) / 4.0; d_var_temp_2 = (2.0 * (arr_var[i_sb2] + arr_var[i_sb3]) + pow2((arr_avgLuma[i_sb2] - arr_avgLuma[i_sb3]))) / 4.0; if (p_avgLumaC) { *p_avgLumaC = (d_avg_temp_1 + d_avg_temp_2) / 2.0; } *p_varianceC = (2.0 * (d_var_temp_1 + d_var_temp_2) + pow2(d_avg_temp_1 - d_avg_temp_2)) / 4.0; } /*! * \brief Function to combine the variance of the mean values of the sub block. * * \param arr_avgLuma Array of 4*4 mean values of the LCU. * \param i_sb0 Index of the sub_blocks 0 in the array of avg values . * \param i_sb1 Index of the sub_blocks 1 in the array of avg values. * \param i_sb2 Index of the sub_blocks 2 in the array of avg values * \param i_sb3 Index of the sub_blocks 3 in the array of avg values * \return variance of the average of the sub blocks. */ static INLINE double features_get_var_of_sub_mean(double* arr_avgLuma, int16_t i_sb0, int16_t i_sb1, int16_t i_sb2, int16_t i_sb3) { double d_var = 0.0; double d_avg = (arr_avgLuma[i_sb0] + arr_avgLuma[i_sb1] + arr_avgLuma[i_sb2] + arr_avgLuma[i_sb3]) / 4.0; d_var = pow2(arr_avgLuma[i_sb0] - d_avg); d_var = pow2(arr_avgLuma[i_sb1] - d_avg) + d_var; d_var = pow2(arr_avgLuma[i_sb2] - d_avg) + d_var; d_var = pow2(arr_avgLuma[i_sb3] - d_avg) + d_var; return d_var / 4.0; } /*! * \brief Build the neighboring variances of four cu's. * * \param arr_features Array of features for current depth. * \param _x X position of the first cu in the array. * \param _y Y position of the first cu in the array. * \param _depth Evaluated depth. * \return None. */ static void features_var_neighbor(features_s* arr_features, int16_t _x, int16_t _y, int16_t _depth) { int16_t i_cu0 = (_x - 1) + ((_y - 1) << _depth); int16_t i_cu1 = (_x)+((_y - 1) << _depth); int16_t i_cu2 = (_x - 1) + (_y << _depth); int16_t i_cu3 = _x + (_y << _depth); arr_features[i_cu0].neigh_variance_A = arr_features[i_cu1].variance; arr_features[i_cu0].neigh_variance_B = arr_features[i_cu2].variance; arr_features[i_cu0].neigh_variance_C = arr_features[i_cu3].variance; arr_features[i_cu1].neigh_variance_A = arr_features[i_cu0].variance; arr_features[i_cu1].neigh_variance_B = arr_features[i_cu2].variance; arr_features[i_cu1].neigh_variance_C = arr_features[i_cu3].variance; arr_features[i_cu2].neigh_variance_A = arr_features[i_cu0].variance; arr_features[i_cu2].neigh_variance_B = arr_features[i_cu1].variance; arr_features[i_cu2].neigh_variance_C = arr_features[i_cu3].variance; arr_features[i_cu3].neigh_variance_A = arr_features[i_cu0].variance; arr_features[i_cu3].neigh_variance_B = arr_features[i_cu1].variance; arr_features[i_cu3].neigh_variance_C = arr_features[i_cu2].variance; } /*! * \brief Extract the features from the pixels for a given different depth. * * \param arr_features Array of features to be retrieved for the current depth. * \param i_depth Depth to be evaluated. * \param arr_var Array of 16*16 variances. * \param arr_avg Array of 16*16 average lumas. * \return None. */ static void features_compute(features_s* arr_features, uint8_t i_depth, double* arr_var, double* arr_avg) { double d_avgLumaC; int8_t i_nbBlock = (1 << i_depth); for (int8_t y = 0; y < i_nbBlock; ++y) { for (int8_t x = 0; x < i_nbBlock; ++x) { int16_t i_cu = x + (y << i_depth); if (i_depth == 4) { arr_features[i_cu].variance = arr_var[i_cu]; } else { features_combine_var(arr_var, arr_avg, x, y, i_depth, &arr_features[i_cu].variance, &d_avgLumaC); int16_t i_CU_4 = (x << (4 - i_depth)) + (y << (8 - i_depth)); int16_t i_rows = (16 << (3 - i_depth)); arr_features[i_cu].var_of_sub_mean = features_get_var_of_sub_mean(arr_avg, i_CU_4, i_CU_4 + (1 << (3 - i_depth)), i_CU_4 + i_rows, i_CU_4 + i_rows + (1 << (3 - i_depth))); arr_avg[i_CU_4] = d_avgLumaC; arr_var[i_CU_4] = arr_features[i_cu].variance; } if (x % 2 == 1 && y % 2 == 1) { features_var_neighbor(arr_features, x, y, i_depth); } } } } /*! * \brief Set the features Sub_var from the sub level for a given different depth. * * \param arr_features Array of features to be retrieved for the current depth. * \param arr_sub_features Array of features to be retrieved for the sub depth (depth - 1). * \param i_rdepth Depth to be evaluated. * \return None. */ static void features_sub_var(features_s* arr_features, features_s* arr_sub_features, uint8_t i_depth) { int8_t i_nbBlock = (1 << i_depth); for (int8_t y = 0; y < i_nbBlock; ++y) { for (int8_t x = 0; x < i_nbBlock; ++x) { int16_t i_cu = x + (y << i_depth); int16_t i_sb0 = (x << 1) + (y << (2 + i_depth)); /*!< Top left sub block index */ int16_t i_sb1 = (x << 1) + 1 + (y << (2 + i_depth)); /*!< Top right sub block index */ int16_t i_sb2 = (x << 1) + (((y << 1) + 1) << (1 + i_depth)); /*!< Bottom left sub block index */ int16_t i_sb3 = (x << 1) + 1 + (((y << 1) + 1) << (1 + i_depth)); /*!< Bottom right sub block index */ arr_features[i_cu].sub_variance_0 = arr_sub_features[i_sb0].variance; arr_features[i_cu].sub_variance_1 = arr_sub_features[i_sb1].variance; arr_features[i_cu].sub_variance_2 = arr_sub_features[i_sb2].variance; arr_features[i_cu].sub_variance_3 = arr_sub_features[i_sb3].variance; } } } /*! * \brief Set the features Merge_var from the up level for a given different depth. * * \param arr_features Array of features to be retrieved for the current depth. * \param arr_up_features Array of features to be retrieved for the upper depth (depth - 1). * \param i_rdepth Depth to be evaluated. * \return None. */ static void features_merge_var(features_s* arr_features, features_s* arr_up_features, uint8_t i_rdepth) { uint8_t i_depth = i_rdepth - 1; int8_t i_nbBlock = (1 << i_depth); for (int8_t y = 0; y < i_nbBlock; ++y) { for (int8_t x = 0; x < i_nbBlock; ++x) { int16_t i_cu = x + (y << i_depth); int16_t i_sb0 = (x << 1) + (y << (2 + i_depth)); /*!< Top left sub block index */ int16_t i_sb1 = (x << 1) + 1 + (y << (2 + i_depth)); /*!< Top right sub block index */ int16_t i_sb2 = (x << 1) + (((y << 1) + 1) << (1 + i_depth)); /*!< Bottom left sub block index */ int16_t i_sb3 = (x << 1) + 1 + (((y << 1) + 1) << (1 + i_depth)); /*!< Bottom right sub block index */ arr_features[i_sb0].merge_variance = arr_up_features[i_cu].variance; arr_features[i_sb1].merge_variance = arr_up_features[i_cu].variance; arr_features[i_sb2].merge_variance = arr_up_features[i_cu].variance; arr_features[i_sb3].merge_variance = arr_up_features[i_cu].variance; } } } /*! * \brief Set the features Var_of_sub_var from the sub level for a given different depth. * * \param arr_features Array of features to be retrieved for the current depth. * \param i_rdepth Depth to be evaluated. * \return None. */ static void features_var_of_sub_var(features_s* arr_features, uint8_t i_depth) { int8_t i_nbBlock = (1 << i_depth); for (int8_t y = 0; y < i_nbBlock; ++y) { for (int8_t x = 0; x < i_nbBlock; ++x) { int16_t i_cu = x + (y << i_depth); double d_var = 0.0; double d_avg = (arr_features[i_cu].sub_variance_0 + arr_features[i_cu].sub_variance_1 + arr_features[i_cu].sub_variance_2 + arr_features[i_cu].sub_variance_3) / 4.0; d_var = pow2(arr_features[i_cu].sub_variance_0 - d_avg); d_var = pow2(arr_features[i_cu].sub_variance_1 - d_avg) + d_var; d_var = pow2(arr_features[i_cu].sub_variance_2 - d_avg) + d_var; d_var = pow2(arr_features[i_cu].sub_variance_3 - d_avg) + d_var; arr_features[i_cu].var_of_sub_var = d_var / 4.0; } } } /*! * \brief Extract the features from the pixels for all the depth. * * \param main_handler Pointer to the main high level reduction handler. * \param p_state Pointer to the state of the current LCU. * \param arr_features_4 Array of features for level of depth 4. * \param arr_features_8 Array of features for level of depth 3. * \param arr_features_16 Array of features for level of depth 2. * \param arr_features_32 Array of features for level of depth 1. * \param p_features64 Pointer to the features of depth 0. * \return None. */ static void features_compute_all(features_s* arr_features[5], kvz_pixel* luma_px) { uint32_t x_px = 0; /*!< Top left X of the lcu */ uint32_t y_px = 0; /*!< Top left Y of the lcu */ double variance[256] = { 0.0 }; double avg_luma[256] = { 0.0 }; features_s* arr_features_4 = arr_features[4]; features_s* arr_features_8 = arr_features[3]; features_s* arr_features_16 = arr_features[2]; features_s* arr_features_32 = arr_features[1]; features_s* p_features64 = arr_features[0]; /*!< Compute the variance for all 4*4 blocs */ for (int8_t y = 0; y < 8; ++y) { for (int8_t x = 0; x < 8; ++x) { int16_t x_blck = (x << 1); int16_t y_blck = (y << 1); features_var_avg_blck(luma_px, x_px, y_px, x_blck << 2, y_blck << 2, 4, LCU_WIDTH, LCU_WIDTH, &avg_luma[CR_GET_CU_D4(x_blck, y_blck, 4)], &variance[CR_GET_CU_D4(x_blck, y_blck, 4)]); features_var_avg_blck(luma_px, x_px, y_px, (x_blck + 1) << 2, y_blck << 2, 4, LCU_WIDTH, LCU_WIDTH, &avg_luma[CR_GET_CU_D4(x_blck + 1, y_blck, 4)], &variance[CR_GET_CU_D4(x_blck + 1, y_blck, 4)]); features_var_avg_blck(luma_px, x_px, y_px, x_blck << 2, (y_blck + 1) << 2, 4, LCU_WIDTH, LCU_WIDTH, &avg_luma[CR_GET_CU_D4(x_blck, y_blck + 1, 4)], &variance[CR_GET_CU_D4(x_blck, y_blck + 1, 4)]); features_var_avg_blck(luma_px, x_px, y_px, (x_blck + 1) << 2, (y_blck + 1) << 2, 4, LCU_WIDTH, LCU_WIDTH, &avg_luma[CR_GET_CU_D4(x_blck + 1, y_blck + 1, 4)], &variance[CR_GET_CU_D4(x_blck + 1, y_blck + 1, 4)]); } } /* Compute the generic features of the all depth */ features_compute(arr_features_4, 4, variance, avg_luma); features_compute(arr_features_8, 3, variance, avg_luma); features_compute(arr_features_16, 2, variance, avg_luma); features_compute(arr_features_32, 1, variance, avg_luma); features_compute(p_features64, 0, variance, avg_luma); /* Set the Sub_var features for the depth 3, 2, 1, 0*/ features_sub_var(arr_features_8, arr_features_4, 3); features_sub_var(arr_features_16, arr_features_8, 2); features_sub_var(arr_features_32, arr_features_16, 1); features_sub_var(p_features64, arr_features_32, 0); /* Set the Merge_var features for the depth 4, 3, 2, 1*/ features_merge_var(arr_features_4, arr_features_8, 4); features_merge_var(arr_features_8, arr_features_16, 3); features_merge_var(arr_features_16, arr_features_32, 2); features_merge_var(arr_features_32, p_features64, 1); /* Compute the Var_of_sub_var for the depth 3, 2, 1, 0*/ features_var_of_sub_var(arr_features_8, 3); features_var_of_sub_var(arr_features_16, 2); features_var_of_sub_var(arr_features_32, 1); features_var_of_sub_var(p_features64, 0); } /*! * \brief Compute the constrain on the neighboring depth of a cu for * a given depth for a BU approach * * \param arr_depthMap 8*8 depth map. * \param _x X coordinate of the cu in the 8*8 depth map; * \param _y Y coordinate of the cu in the 8*8 depth map; * \param _depth Current depth tested. * \param _level number of depth gap that we want * \return 1 if the predictions should be tested for this cu, 0 else. */ static int neighbor_constrain_bu(uint8_t* arr_depthMap, int _x, int _y, int _depth, int _level) { int nb_block = (8 >> (_depth)) << 1; for (int y = _y; y < _y + nb_block; ++y) { for (int x = _x; x < _x + nb_block; ++x) { if (arr_depthMap[x + (y << 3)] - _level >= _depth) return 0; } } return 1; } static int8_t combined_tree_function(int8_t merge_prediction[4], int8_t split_prediction, uint8_t test_id, uint8_t depth) { int8_t prediction; int8_t pred_merge_tmp = 0; // NUmber of sub-blocks non merge (=d) for (int8_t i = 0; i < 4; i++) { pred_merge_tmp += (merge_prediction[i] > 0) ? 1 : 0; } switch (test_id) {// We don't merge (-1) if : case 0: // At least one sub block non merge prediction = (pred_merge_tmp >= 1) ? depth : -1; break; case 1: // At least two sub blocks non merge prediction = (pred_merge_tmp >= 2) ? depth : -1; break; case 2: // At least three sub blocks non merge prediction = (pred_merge_tmp >= 3) ? depth : -1; break; case 3: // All sub blocks non merge prediction = (pred_merge_tmp >= 4) ? depth : -1; break; case 4: // Up bock non merge ( = split) prediction = (split_prediction == -1) ? depth : -1; break; case 5: // (At least one sub block non merge) & Up block non merge prediction = ((pred_merge_tmp >= 1) && (split_prediction == -1)) ? depth : -1; break; case 6: // (At least two sub blocks non merge) & Up block non merge prediction = ((pred_merge_tmp >= 2) && (split_prediction == -1)) ? depth : -1; break; case 7: // (At least three sub blocks non merge) & Up block non merge prediction = ((pred_merge_tmp >= 3) && (split_prediction == -1)) ? depth : -1; break; case 8: // (All sub blocks non merge) & Up block non merge prediction = ((pred_merge_tmp >= 4) && (split_prediction == -1)) ? depth : -1; break; case 9: // (At least one sub block non merge) | Up block non merge prediction = ((pred_merge_tmp >= 1) || (split_prediction == -1)) ? depth : -1; break; case 10: // (At least two sub blocks non merge) | Up block non merge prediction = ((pred_merge_tmp >= 2) || (split_prediction == -1)) ? depth : -1; break; case 11: // (At least three sub blocks non merge) | Up block non merge prediction = ((pred_merge_tmp >= 3) || (split_prediction == -1)) ? depth : -1; break; case 12: // (All sub blocks non merge) | Up block non merge prediction = ((pred_merge_tmp >= 4) || (split_prediction == -1)) ? depth : -1; break; default: prediction = 0; } return prediction; } static void fill_depth_matrix_8(uint8_t* matrix, vect_2D* cu, int8_t curr_depth, int8_t val) { //convert cu coordinate int32_t x = cu->x; int32_t y = cu->y; int i = 0; int32_t block = (8 >> curr_depth); //nb blocks in 8*8 block for (i = y; i < y + block; ++i) { memset(matrix + x + (i << 3), val, block); } } /*! * \brief Generate the PUM depth map in a 8*8 array for a given depth with a Buttom-Up approach. * * \param arr_depthMap Array of the depth map. * \param arr_features_cur Array of features for current depth (i_depth). * \param arr_features_up Array of features for up depth (i_depth-1). * \param i_depth Current depth tested. * \param _level Number of level tested when the algo is Restrained (limited) * \param limited_flag 0 to not test that the 4 blocks are at the same depth * 1 to only merge a bloc if the 4 sub blocks are at the same depth * \param depth_flag 0 to not use depth features * 1 to use use depth features * \return None. */ static void ml_os_qt_gen(uint8_t* arr_depthMap, features_s* arr_features_cur, features_s* arr_features_up, uint8_t i_depth, int _level, uint8_t limited_flag) { tree_predict predict_func_merge[4] = { tree_predict_merge_depth_1, tree_predict_merge_depth_2, tree_predict_merge_depth_3, tree_predict_merge_depth_4 }; tree_predict predict_func_split[4] = { tree_predict_split_depth_0, tree_predict_split_depth_1, tree_predict_split_depth_2, tree_predict_split_depth_3 }; tree_predict prediction_function_merge = predict_func_merge[i_depth - 1]; tree_predict prediction_function_split = predict_func_split[i_depth - 1]; double d_nb_iter; double d_nb_bad; uint8_t i_rdepth = i_depth < 4 ? i_depth : 3; int16_t i_nbBlocks = 2 << (i_depth - 1); int inc = 2; for (int16_t y = 0; y < i_nbBlocks; y += inc) { for (int16_t x = 0; x < i_nbBlocks; x += inc) { uint8_t check_flag = 1; /*!< Check if neighboring blocks are of the same size */ if ((limited_flag == 1) && (i_depth != 4)) { check_flag = neighbor_constrain_bu(arr_depthMap, x << (3 - i_depth), y << (3 - i_depth), i_depth, _level); } if (check_flag) { int16_t i_cu_0 = x + (y << i_depth); int16_t i_cu_1 = x + 1 + (y << i_depth); int16_t i_cu_2 = x + ((y + 1) << i_depth); int16_t i_cu_3 = x + 1 + ((y + 1) << i_depth); int16_t i_cu_up = x / 2 + (y / 2 << (i_depth - 1)); int8_t merge_prediction[4]; int8_t split_prediction; merge_prediction[0] = prediction_function_merge(&arr_features_cur[i_cu_0], &d_nb_iter, &d_nb_bad); merge_prediction[1] = prediction_function_merge(&arr_features_cur[i_cu_1], &d_nb_iter, &d_nb_bad); merge_prediction[2] = prediction_function_merge(&arr_features_cur[i_cu_2], &d_nb_iter, &d_nb_bad); merge_prediction[3] = prediction_function_merge(&arr_features_cur[i_cu_3], &d_nb_iter, &d_nb_bad); split_prediction = prediction_function_split(&arr_features_up[i_cu_up], &d_nb_iter, &d_nb_bad); int8_t pred = combined_tree_function(merge_prediction, split_prediction, (i_depth >= 4) ? 8 : 9, i_depth); int condition = (pred < 0) ? 1 : 0; if (condition) { int16_t i_subCU = CR_GET_CU_D3((i_depth < 4 ? x : x / 2), (i_depth < 4 ? y : y / 2), i_rdepth); vect_2D tmp; tmp.x = i_subCU % 8; tmp.y = i_subCU / 8; fill_depth_matrix_8(arr_depthMap, &tmp, i_depth - 1, i_depth - 1); } } } } } static void os_luma_qt_pred(ml_intra_ctu_pred_t* ml_intra_depth_ctu, kvz_pixel* luma_px, int8_t qp, uint8_t* arr_CDM) { // Features array per depth features_s arr_features_4[256]; features_s arr_features_8[64]; features_s arr_features_16[16]; features_s arr_features_32[4]; features_s features64; // Initialize to 0 all the features features_init_array(arr_features_4, 256, qp); features_init_array(arr_features_8, 64, qp); features_init_array(arr_features_16, 16, qp); features_init_array(arr_features_32, 4, qp); features_init_array(&features64, 1, qp); // Commpute the features for the current CTU for all depth features_s* arr_features[5]; arr_features[0] = &features64; arr_features[1] = arr_features_32; arr_features[2] = arr_features_16; arr_features[3] = arr_features_8; arr_features[4] = arr_features_4; features_compute_all(arr_features, luma_px); // Generate the CDM for the current CTU /*!< Set the depth map to 4 by default */ memset(arr_CDM, 4, 64); ml_os_qt_gen(arr_CDM, arr_features_4, arr_features_8, 4, 1, RESTRAINED_FLAG); ml_os_qt_gen(arr_CDM, arr_features_8, arr_features_16, 3, 1, RESTRAINED_FLAG); ml_os_qt_gen(arr_CDM, arr_features_16, arr_features_32, 2, 1, RESTRAINED_FLAG); ml_os_qt_gen(arr_CDM, arr_features_32, &features64, 1, 1, RESTRAINED_FLAG); } static void fill_matrix_with_depth(uint8_t* matrix, int32_t x, int32_t y, int8_t depth) { int i = 0; int32_t block = depth < 4 ? (8 >> depth) : 1; //nb blocks in 8*8 block for (i = y; i < y + block; ++i) { memset(matrix + x + (i << 3), depth, block); } } /*! * \brief Merge the depth of the blocks of a depth map if * four blocks of the same depths are found. * * \param _mat_seed Array of the depth used as seed for the merge (WARNING: must be the same as arrDepthMerge (tmp)). * \param _mat_dst Array of the depth merged. * \return 1 if blocks have been merged, 0 else. */ static uint8_t merge_matrix_64(uint8_t* _mat_seed, uint8_t* _mat_dst) { uint8_t i_depth = 0; uint32_t nb_block = 0; uint8_t retval = 0; uint8_t mat_tmp[64]; memcpy(mat_tmp, _mat_seed, 64); for (uint_fast8_t i_y = 0; i_y < 8; ++i_y) { for (uint_fast8_t i_x = 0; i_x < 8; ++i_x) { i_depth = mat_tmp[i_x + (i_y << 3)]; if (i_depth == 4) { _mat_dst[i_x + (i_y << 3)] = 3;/*!< All depth 4 blocks are merged by default to depth 3 */ retval = 1; continue; /*!< Skip the modulo operations and conditional tests */ } if (i_depth == 0) /*!< Skip all the loop process, since 0 depth means there will be no other depths tested */ { _mat_dst[i_x + (i_y << 3)] = i_depth; memset(_mat_dst, 0, 64); goto exit_64; } nb_block = (16 >> i_depth); /*!< Offset to go check the three other blocks */ /*!< Check if we are on the fourth block of a depth*/ if ((i_x % nb_block == (8 >> i_depth)) && (i_y % nb_block == (8 >> i_depth))) { retval = 1; nb_block = (8 >> i_depth); /*!< Generate the real offset for the array */ /* * x 0 1 2 3 4 5 6 7 * y * 0 3 3 2 2 1 1 1 1 * 1 3 3 2 2 1 1 1 1 * 2 2 2 2 2 1 1 1 1 * 3 2 2 2 2 1 1 1 1 * 4 1 1 1 1 2 2 2 2 * 5 1 1 1 1 2 2 2 2 * 6 1 1 1 1 2 2 2 2 * 7 1 1 1 1 2 2 2 2 * * exemple for the first fourth block of depth 2 : * 8 >> 2 = 2 * nb_block = 4 -> x % 4 == 2 -> x = 2 * -> y % 4 == 2 -> y = 2 * nb_block = 2 -> check blocs[(0,2),(2,0),(0,0)] * all informations are available */ if (mat_tmp[i_x - nb_block + (i_y << 3)] == i_depth && mat_tmp[i_x + ((i_y - nb_block) << 3)] == i_depth && mat_tmp[i_x - nb_block + ((i_y - nb_block) << 3)] == i_depth) { fill_matrix_with_depth(_mat_dst, i_x - nb_block, i_y - nb_block, i_depth - 1); } } } } exit_64: return retval; } /*! * \brief Perform an in place element wise mask between the two matrix. * * \param _mat_mask Matrix containing result of the mask (input/output). * \param _mat_src Matrix used for the mask (input). * \param _size_w Width of the matrix. * \param _size_h Height of the matrix. * \return None. */ static void matrix_mask(uint8_t* _mat_mask, const uint8_t* _mat_src, size_t _size_w, size_t _size_h) { if (_mat_mask == NULL || _mat_src == NULL) { fprintf(stderr, "null pointer as parameter."); assert(0); return; } size_t i_size = _size_h * _size_w; for (size_t i = 0; i < i_size; ++i) { _mat_mask[i] = (_mat_mask[i] ^ _mat_src[i]) != 0 ? 1 : 0; } } /*! * \brief Add 1 depth level to the depth map. If d + 1 > 4 then d - 1 is done. * This function use a mask to add level only on selected roi. * * \param _mat_sup Original upper depth map . * \param _mat_inf Lower depth map. * \param _mat_sup_dst Final upper depth map (WARNING: must be a different array as _mat_sup as it can be modified). * \param _nb_level The number of level there should be between inf and sup_dst. * \param _mat_roi Mask used to determine which area should be modified on the _mat_inf (convention is 0 for changed area and 1 else). * \return None. */ static void matrix_add_level_roi(const uint8_t* _mat_sup, uint8_t* _mat_inf, uint8_t* _mat_sup_dst, int8_t _nb_level, const uint8_t* _mat_roi) { int8_t x = 0, y = 0; int8_t i_depth = 0; for (y = 0; y < 8; ++y) { for (x = 0; x < 8; ++x) { if ((!_mat_roi[x + (y << 3)]) == 1) { i_depth = _mat_sup[x + (y << 3)]; if (i_depth == 4) { int8_t i_depth_sup = _mat_sup_dst[x + (y << 3)]; _mat_inf[x + (y << 3)] = 4; if (i_depth_sup == 4) { _mat_sup_dst[x + (y << 3)] = 3; } else if (i_depth_sup > 0 && abs(i_depth_sup - 4) < _nb_level) { fill_matrix_with_depth(_mat_sup_dst, (x & (~(8 >> (i_depth_sup)))), (y & (~(8 >> (i_depth_sup)))), i_depth_sup - 1); } continue; } else if (i_depth == 3) { _mat_inf[x + (y << 3)] = 4; continue; } else if (abs(_mat_inf[x + (y << 3)] - _mat_sup[x + (y << 3)]) != _nb_level) { fill_matrix_with_depth(_mat_inf, x, y, i_depth + 1); } x += (8 >> (i_depth + 1)) - 1; } } } } /*! * \brief Generate a search interval of controlled level around a MEP seed. * * \param _mat_depth_min Upper depth map (considered as the MEP on call). * \param _mat_depth_max Lower depth map (considered initialized with the MEP values). * \param _nb_level Fixed distance between the two generated depth map. * \return None. */ static void generate_interval_from_os_pred(ml_intra_ctu_pred_t* ml_intra_depth_ctu, uint8_t* _mat_depth_MEP) { uint8_t* _mat_depth_min = ml_intra_depth_ctu->_mat_upper_depth; uint8_t* _mat_depth_max = ml_intra_depth_ctu->_mat_lower_depth; int8_t _nb_level = ml_intra_depth_ctu->i_nb_addDepth; memcpy(_mat_depth_min, _mat_depth_MEP, 64 * sizeof(uint8_t)); memcpy(_mat_depth_max, _mat_depth_MEP, 64 * sizeof(uint8_t)); if (_nb_level <= 0) { return; } else if (_nb_level >= 4) { memset(_mat_depth_min, 0, 64 * sizeof(uint8_t)); memset(_mat_depth_max, 4, 64 * sizeof(uint8_t)); return; } uint8_t mat_ref[64]; /*!< Matrix used to store the ref map */ uint8_t mat_mask[64]; /*!< Matrix used as mask */ uint8_t mat_max[64]; /*!< Matrix used to store current depth map max */ for (int j = 0; j < _nb_level; ++j) { /*!< Copy the original map seed */ memcpy(mat_ref, _mat_depth_min, 64 * sizeof(uint8_t)); memcpy(mat_mask, _mat_depth_min, 64 * sizeof(uint8_t)); memcpy(mat_max, _mat_depth_max, 64 * sizeof(uint8_t)); /*!< Apply the RCDM on the upper map */ merge_matrix_64(_mat_depth_min, _mat_depth_min); /*!< Extract the mask */ matrix_mask(mat_mask, _mat_depth_min, 8, 8); /*!< Add a level only on the masked area */ matrix_add_level_roi(mat_max, _mat_depth_max, _mat_depth_min, 1, mat_mask); } } /** * Generate the interval of depth predictions based on the luma samples */ void kvz_lcu_luma_depth_pred(ml_intra_ctu_pred_t* ml_intra_depth_ctu, kvz_pixel* luma_px, int8_t qp) { // Compute the one-shot (OS) Quad-tree prediction (_mat_OS_pred) os_luma_qt_pred(ml_intra_depth_ctu, luma_px, qp, ml_intra_depth_ctu->_mat_upper_depth); // Generate the interval of QT predictions around the first one generate_interval_from_os_pred(ml_intra_depth_ctu, ml_intra_depth_ctu->_mat_upper_depth); // Apply the extra Upper Expansion pass merge_matrix_64(ml_intra_depth_ctu->_mat_upper_depth, ml_intra_depth_ctu->_mat_upper_depth); }