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RISS 인기검색어
- 검색키워드
- 저자 : ( Zongju Peng ) (검색결과 5 건)
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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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Bayesian-theory-based Fast CU Size and Mode Decision Algorithm for 3D-HEVC Depth Video Inter-coding
( Fen Chen ),( Sheng Liu ),( Zongju Peng ),( Qingqing Hu ),( Gangyi Jiang ),( Mei Yu ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.4
Multi-view video plus depth (MVD) is a mainstream format of 3D scene representation in free viewpoint video systems. The advanced 3D extension of the high efficiency video coding (3D-HEVC) standard introduces new prediction tools to improve the coding performance of depth video. However, the depth video in 3D-HEVC is time consuming. To reduce the complexity of the depth video inter coding, we propose a fast coding unit (CU) size and mode decision algorithm. First, an off-line trained Bayesian model is built which the feature vector contains the depth levels of the corresponding spatial, temporal, and inter-component (texture-depth) neighboring largest CUs (LCUs). Then, the model is used to predict the depth level of the current LCU, and terminate the CU recursive splitting process. Finally, the CU mode search process is early terminated by making use of the mode correlation of spatial, inter-component (texture-depth), and inter-view neighboring CUs. Compared to the 3D-HEVC reference software HTM-10.0, the proposed algorithm reduces the encoding time of depth video and the total encoding time by 65.03% and 41.04% on average, respectively, with negligible quality degradation of the synthesized virtual view.
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Fast Macroblock Mode Selection Algorithm for B Frames in Multiview Video Coding
( Mei Yu ),( Ping He ),( Zongju Peng ),( Yun Zhang ),( Yuehou Si ),( Gangyi Jiang ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.2
Intensive computational complexity is an obstacle of enabling multiview video coding for real-time applications. In this paper, we present a fast macroblock (MB) mode selection algorithm for B frames which are based on the computational complexity analyses between the MB mode selection and reference frame selection. Three strategies are proposed to reduce the coding complexity jointly. First, the temporal correlation of MB modes between current MB and its temporal corresponding MBs is utilized to reduce computational complexity in determining the optimal MB mode. Secondly, Lagrangian cost of SKIP mode is compared with that of Inter16×16 modes to early terminate the mode selection process. Thirdly, reference frame correlation among different Inter modes is exploited to reduce the number of reference frames. Experimental results show that the proposed algorithm can promote the encoding speed by 3.71~7.22 times with 0.08dB PSNR degradation and 2.03% bitrate increase on average compared with the joint multiview video model.
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Yun Zhang,Gangyi Jiang,Mei Yu,Zongju Peng 대한전자공학회 2009 ITC-CSCC :International Technical Conference on Ci Vol.2009 No.7
Region-of-interest (ROI) in multiview video is different from that of conventional single view video because MVV provides three-dimensional perception and makes people more interested in depth discontinuity and regions close to viewers. In this paper, we present depth based ROI extraction algorithm semantic depth perceptual ROIs by jointly using depth, motion and texture information of multiview video plus depth data. To reduce the computational complexity and improve ROI extraction efficiency for group of pictures, we present a novel interview tracking method, in which geometry correlation between views and extracted ROI of neighboring views are utilized to facilitate ROI extraction in view dimension. ROI extraction results for multiview videos show that the proposed ROI tracking and extraction algorithms maintain high extraction accuracy and low complexity.
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A Perception-based Color Correction Method for Multi-view Images
( Feng Shao ),( Gangyi Jiang ),( Mei Yu ),( Zongju Peng ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.2
Three-dimensional (3D) video technologies are becoming increasingly popular, as it can provide users with high quality and immersive experiences. However, color inconsistency between the camera views is an urgent problem to be solved in multi-view imaging. In this paper, a perception-based color correction method for multi-view images is proposed. In the proposed method, human visual sensitivity (VS) and visual attention (VA) models are incorporated into the correction process. Firstly, the VS property is used to reduce the computational complexity by removing these visual insensitive regions. Secondly, the VA property is used to improve the perceptual quality of local VA regions by performing VA-dependent color correction. Experimental results show that compared with other color correction methods, the proposed method can greatly promote the perceptual quality of local VA regions greatly and reduce the computational complexity, and obtain higher coding performance.
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