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
Figure 7.11: Graphical representation of Time vs QP for SlideEditing Figure 7.12: Graphical representation of PSNR vs QP for SlideEditing SlideEditing_1280x720_30 frame 100 Fig. 7.13 SlideEditing_1280x720_30.yuv Original Fig. 7.14 SlideEditing_1280x720_30.yuv SAO=0 Fig.7.15 SlideEditing_1280x720_30.yuv SAO=1 Parallel code results
BIOGRAPHICAL INFORMATION Harsha Nagathihalli Jagadish was born in Bengaluru, Karnataka, India. His Schooling was done in Florence Public School, Bengaluru. He went on to pursue his Bachelor of Engineering in Electronics and Communications from B.M.S. Institute of Technology (Affiliated to Visvesvaraya Technological University). Previously, he worked as a Programmer Analyst in Cognizant Technology Solutions, Chennai, India from 2014 to 2015. Harsha then enrolled to Masters in Electrical Engineering at University of Texas at Arlington in fall 2015. During the course of the Masters he nurtured his skills in the field of Multimedia and Embedded Systems. He joined Multimedia Processing Lab under the guidance of Dr. K.R. Rao who is one of the founders of Discrete Cosine Transform (DCT). He also worked as a Graduate Teaching Assistant under Dr. Rao for Digital Image Processing and Digital Video Coding in Fall 2016 and Spring 2017. Harsha worked with CalAmp as a Software Engineer Intern in Summer 2017 and Fall 2017. His research activities include areas of both Multimedia and Embedded systems. His recent research