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Fast Mode Decision For Depth Video Coding Based On Depth Segmentation
( Yequn Wang ),( Zongju Peng ),( Gangyi Jiang ),( Mei Yu ),( Feng Shao ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.4
With the development of three-dimensional display and related technologies, depth video coding becomes a new topic and attracts great attention from industries and research institutes. Because (1) the depth video is not a sequence of images for final viewing by end users but an aid for rendering, and (2) depth video is simpler than the corresponding color video, fast algorithm for depth video is necessary and possible to reduce the computational burden of the encoder. This paper proposes a fast mode decision algorithm for depth video coding based on depth segmentation. Firstly, based on depth perception, the depth video is segmented into three regions: edge, foreground and background. Then, different mode candidates are searched to decide the encoding macroblock mode. Finally, encoding time, bit rate and video quality of virtual view of the proposed algorithm are tested. Experimental results show that the proposed algorithm save encoding time ranging from 82.49% to 93.21% with negligible quality degradation of rendered virtual view image and bit rate increment.
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A Rapid Convergent Max-SINR Algorithm for Interference Alignment Based on Principle Direction Search
( Zhilu Wu ),( Lihui Jiang ),( Guanghui Ren ),( Gangyi Wang ),( Nan Zhao ),( Yaqin Zhao ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5
The maximal signal-to-interference-plus-noise ratio (Max-SINR) algorithm for interference alignment (IA) has received considerable attention for its high sum rate achievement in the multiple-input multiple-output (MIMO) interference channel. However, its complexity may increase dramatically when the number of users approaches the IA feasibility bound, and the number of iterations and computational time may become unacceptable. In this paper, we study the properties of the Max-SINR algorithm thoroughly by presenting theoretical insight into the algorithm and by providing the potential of reducing the overall computational cost. Furthermore, a novel IA algorithm based on the principle direction search is proposed, which can converge more rapidly than the conventional Max-SINR method. In the proposed algorithm, it searches along the principle direction, which is found to approximately point to the convergence values, and can approach the convergence solutions rapidly. In addition, the closed-form solution of the optimal step size can be formulated in the sense of minimal interference leakage. Simulation results demonstrate that the proposed algorithm outperforms the conventional minimal interference leakage and Max-SINR algorithms in terms of the convergence rate while guaranteeing the high throughput of IA networks.
ScienceON
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