In and are the number of samples and the sum of Parallel Algorithm (fig. 6.2), Nipq Eipq differences of the category for the class in the CTB, respectively. and q-th p-th i-th Nijkm are the statistics of the corresponding each sub-CTB. We can see that this parallel Eijkm algorithm consists of two main steps. The first step has parallel branches NCTBNsub-CTBNEO with each one for computing the statistics for each EO class of each sub-CTB in each CTB and the parallel degree is The second step contains 5NCTBNEO parallel NCTBNsub-CTBNEO. branches with each branch for completing the statistics computation for each CTB and the parallel degree is 5 For 1920×1080 (i.e. HD) video sequences, if we set the size of NCTBNEO. CTB to 16×16 and to 16, is 8160, the parallel degree of the first step is Nsub-CTB NCTB 522240 and the parallel degree of the second step is 163200. In the same way as the design of shown in the sample classification Parallel Algorithm fig. 6.2, and statistics collection of BO is also performed in parallel for each CTB. The parallel algorithm for realizing this operation procedure is given below (where denotes the Nband number of BO bands). Figure 6.3: Parallel Algorithm 6(b) [1] In and are the number of samples and the sum of Parallel Algorithm fig. 6.3, Nip Eip differences of the band in the CTB, respectively and and are the statistics of p-th i-th Nijk Eijk the corresponding each sub-CTB. We can see that this parallel algorithm also contains two main computation steps. The first step has parallel branches with each branch NCTBNsub-CTB for computing the BO statistics for each sub-CTB in each CTB and the parallel degree is The second step contains parallel branches with each one for NCTBNsub-CTB. NCTBNband completing the statistics computation for each CTB and the parallel degree is NCTBNband. Parallel Algorithm for Obtaining the Best EO Parameters The objective of this subsection is to design a parallel algorithm to compute the best EO parameters including the best offset values and corresponding distortions for each category in
In Parallel Algorithm (fig. 6.2), Nipq and Eipq are the number of samples and the sum of differences of the q-th category for the p-th class in the i-th CTB, respectively. Nijkm and Eijkm are the statistics of the corresponding each sub-CTB. We can see that this parallel algorithm consists of two main steps. The first step has NCTBNsub-CTBNEO parallel branches with each one for computing the statistics for each EO class of each sub-CTB in each CTB and the parallel degree is NCTBNsub-CTBNEO. The second step contains 5NCTBNEO parallel branches with each branch for completing the statistics computation for each CTB and the parallel degree is 5 NCTBNEO. For 1920×1080 (i.e. HD) video sequences, if we set the size of CTB to 16×16 and Nsub-CTB to 16, NCTB is 8160, the parallel degree of the first step is 522240 and the parallel degree of the second step is 163200. In the same way as the design of Parallel Algorithm shown in fig. 6.2, the sample classification and statistics collection of BO is also performed in parallel for each CTB. The parallel algorithm for realizing this operation procedure is given below (where Nband denotes the number of BO bands). Figure 6.3: Parallel Algorithm 6(b) [1] In Parallel Algorithm fig. 6.3, Nip and Eip are the number of samples and the sum of differences of the p-th band in the i-th CTB, respectively and Nijk and Eijk are the statistics of the corresponding each sub-CTB. We can see that this parallel algorithm also contains two main computation steps. The first step has NCTBNsub-CTB parallel branches with each branch for computing the BO statistics for each sub-CTB in each CTB and the parallel degree is NCTBNsub-CTB. The second step contains NCTBNband parallel branches with each one for completing the statistics computation for each CTB and the parallel degree is NCTBNband. Parallel Algorithm for Obtaining the Best EO Parameters The objective of this subsection is to design a parallel algorithm to compute the best EO parameters including the best offset values and corresponding distortions for each category in
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