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Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC
3-D High Efficiency Video Coding (HEVC) is a new emerging video compression standard for multiview video applications. This standard utilizes advanced interview prediction characteristics in addition to the prediction features of the HEVC standard for efficient encoding of multiview video content. W...
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Published in: | IEEE transactions on circuits and systems for video technology 2016-10, Vol.26 (10), p.1870-1883 |
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description | 3-D High Efficiency Video Coding (HEVC) is a new emerging video compression standard for multiview video applications. This standard utilizes advanced interview prediction characteristics in addition to the prediction features of the HEVC standard for efficient encoding of multiview video content. While using combined features improves the compression performance by utilizing the correlation between the views captured from slightly different angles of the same scene, they also increase coding complexity. The focus of this paper is on developing an efficient complexity reduction scheme for 3D-HEVC, with the intention to facilitate the adoption of this upcoming standard, especially for real-time applications. In this regard, first, we introduce two ways to decrease the complexity of the inter-/ intra-mode search process of the to-be-encoded blocks in the dependent texture views ({\mathrm {DV}}_{t}\text{s} ) of 3D-HEVC. Then, we propose a hybrid complexity reduction scheme that utilizes the two-mode prediction approaches, motion information of the base texture view (BVt), and the rate distortion cost of the already encoded blocks in the BVt and DVt. The performance of our proposed scheme is tested for the case with two views (i.e., base view + dependent view). The evaluations confirm that our proposed hybrid complexity reduction scheme reduces the 3D-HEVC codec complexity by 67.70% on average for the DVt compared with the unmodified 3D-HEVC encoder, while maintaining the overall video quality. Compared with the state-of-the-art method, it reduces complexity by 25.74% on average. |
doi_str_mv | 10.1109/TCSVT.2015.2477955 |
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Then, we propose a hybrid complexity reduction scheme that utilizes the two-mode prediction approaches, motion information of the base texture view (BVt), and the rate distortion cost of the already encoded blocks in the BVt and DVt. The performance of our proposed scheme is tested for the case with two views (i.e., base view + dependent view). The evaluations confirm that our proposed hybrid complexity reduction scheme reduces the 3D-HEVC codec complexity by 67.70% on average for the DVt compared with the unmodified 3D-HEVC encoder, while maintaining the overall video quality. 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This standard utilizes advanced interview prediction characteristics in addition to the prediction features of the HEVC standard for efficient encoding of multiview video content. While using combined features improves the compression performance by utilizing the correlation between the views captured from slightly different angles of the same scene, they also increase coding complexity. The focus of this paper is on developing an efficient complexity reduction scheme for 3D-HEVC, with the intention to facilitate the adoption of this upcoming standard, especially for real-time applications. In this regard, first, we introduce two ways to decrease the complexity of the inter-/ intra-mode search process of the to-be-encoded blocks in the dependent texture views ({\mathrm {DV}}_{t}\text{s} ) of 3D-HEVC. Then, we propose a hybrid complexity reduction scheme that utilizes the two-mode prediction approaches, motion information of the base texture view (BVt), and the rate distortion cost of the already encoded blocks in the BVt and DVt. The performance of our proposed scheme is tested for the case with two views (i.e., base view + dependent view). The evaluations confirm that our proposed hybrid complexity reduction scheme reduces the 3D-HEVC codec complexity by 67.70% on average for the DVt compared with the unmodified 3D-HEVC encoder, while maintaining the overall video quality. Compared with the state-of-the-art method, it reduces complexity by 25.74% on average.</description><subject>3-D High Efficiency Video Coding (HEVC)</subject><subject>Bayesian classifier</subject><subject>Codec</subject><subject>Coding</subject><subject>Complexity</subject><subject>Complexity theory</subject><subject>Correlation</subject><subject>Defects</subject><subject>Distance learning</subject><subject>Encoding</subject><subject>low-complexity compression</subject><subject>online learning</subject><subject>Predictive models</subject><subject>Probabilistic logic</subject><subject>Reduction</subject><subject>Search process</subject><subject>State of the art</subject><subject>Texture</subject><subject>Video coding</subject><subject>Video compression</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kE1Lw0AQQBdRsFb_gF4Cnrfu7Ed2c_CgsVqhULC11yXZTDSlTepuAvbfm9ri0dPM4b0ZeIRcAxsBsORukc6XixFnoEZcap0odUIGoJShnDN12u9MATUc1Dm5CGHFGEgj9YDcz-p1VSOdYubrqv6gj1nAIkqbzXaN31W7i96w6FxbNXU0d5-4wahsfCSe6GS8TC_JWZmtA14d55C8P48X6YROZy-v6cOUOsFFS1WWoJO5cLGMQUmmFdMy0RqMQ2ZKVcY5moLngK50WKAokCeYJ4CxBs25GJLbw92tb746DK1dNZ2v-5eWg5aCMxn_S4ERwFgSx7Kn-IFyvgnBY2m3vtpkfmeB2X1M-xvT7mPaY8xeujlIFSL-CZorI6QWP6qSbeI</recordid><startdate>201610</startdate><enddate>201610</enddate><creator>Tohidypour, Hamid Reza</creator><creator>Pourazad, Mahsa T.</creator><creator>Nasiopoulos, Panos</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201610</creationdate><title>Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC</title><author>Tohidypour, Hamid Reza ; Pourazad, Mahsa T. ; Nasiopoulos, Panos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c323t-5a9ec4b3c64615407507497718ce08f5f6be8d2b1ecfcede3de29eb91e6717223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>3-D High Efficiency Video Coding (HEVC)</topic><topic>Bayesian classifier</topic><topic>Codec</topic><topic>Coding</topic><topic>Complexity</topic><topic>Complexity theory</topic><topic>Correlation</topic><topic>Defects</topic><topic>Distance learning</topic><topic>Encoding</topic><topic>low-complexity compression</topic><topic>online learning</topic><topic>Predictive models</topic><topic>Probabilistic logic</topic><topic>Reduction</topic><topic>Search process</topic><topic>State of the art</topic><topic>Texture</topic><topic>Video coding</topic><topic>Video compression</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tohidypour, Hamid Reza</creatorcontrib><creatorcontrib>Pourazad, Mahsa T.</creatorcontrib><creatorcontrib>Nasiopoulos, Panos</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on circuits and systems for video technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tohidypour, Hamid Reza</au><au>Pourazad, Mahsa T.</au><au>Nasiopoulos, Panos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC</atitle><jtitle>IEEE transactions on circuits and systems for video technology</jtitle><stitle>TCSVT</stitle><date>2016-10</date><risdate>2016</risdate><volume>26</volume><issue>10</issue><spage>1870</spage><epage>1883</epage><pages>1870-1883</pages><issn>1051-8215</issn><eissn>1558-2205</eissn><coden>ITCTEM</coden><abstract>3-D High Efficiency Video Coding (HEVC) is a new emerging video compression standard for multiview video applications. 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subjects | 3-D High Efficiency Video Coding (HEVC) Bayesian classifier Codec Coding Complexity Complexity theory Correlation Defects Distance learning Encoding low-complexity compression online learning Predictive models Probabilistic logic Reduction Search process State of the art Texture Video coding Video compression |
title | Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC |
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