Improving temporal error concealment by GRNN in video communication
This work aims to improve the temporal error concealment for the corrupted macroblocks whose motions are not locally-smooth. It is demonstrated that the recovered quality by the oft-used motion estimation approaches is not visually satisfied for those MBs of which adjacent MBs do not have a consiste...
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| creator | Jun-Horng Chen Shih-Chun Shao Wen-Hui Chen |
| description | This work aims to improve the temporal error concealment for the corrupted macroblocks whose motions are not locally-smooth. It is demonstrated that the recovered quality by the oft-used motion estimation approaches is not visually satisfied for those MBs of which adjacent MBs do not have a consistent movement. Therefore, this work will propose and demonstrate that, if the conventional error concealment approach is followed by the nonparametric regression approach GRNN, the concealed quality will be raised. The simulation results will show the proposed approach indeed improves the performance of error concealment and the improving gain is about 1 dB of PSNR. |
| doi_str_mv | 10.1109/ICME.2011.6012058 |
| format | Conference Proceeding |
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It is demonstrated that the recovered quality by the oft-used motion estimation approaches is not visually satisfied for those MBs of which adjacent MBs do not have a consistent movement. Therefore, this work will propose and demonstrate that, if the conventional error concealment approach is followed by the nonparametric regression approach GRNN, the concealed quality will be raised. The simulation results will show the proposed approach indeed improves the performance of error concealment and the improving gain is about 1 dB of PSNR.</description><identifier>ISSN: 1945-7871</identifier><identifier>ISBN: 1612843484</identifier><identifier>ISBN: 9781612843483</identifier><identifier>EISSN: 1945-788X</identifier><identifier>EISBN: 1612843506</identifier><identifier>EISBN: 9781612843490</identifier><identifier>EISBN: 9781612843506</identifier><identifier>EISBN: 1612843492</identifier><identifier>DOI: 10.1109/ICME.2011.6012058</identifier><language>eng</language><publisher>IEEE</publisher><subject>error concealment ; Estimation ; general regression neural network ; Joints ; Probabilistic logic ; PSNR ; Simulation ; Training ; Visualization</subject><ispartof>2011 IEEE International Conference on Multimedia and Expo, 2011, p.1-4</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6012058$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6012058$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jun-Horng Chen</creatorcontrib><creatorcontrib>Shih-Chun Shao</creatorcontrib><creatorcontrib>Wen-Hui Chen</creatorcontrib><title>Improving temporal error concealment by GRNN in video communication</title><title>2011 IEEE International Conference on Multimedia and Expo</title><addtitle>ICME</addtitle><description>This work aims to improve the temporal error concealment for the corrupted macroblocks whose motions are not locally-smooth. It is demonstrated that the recovered quality by the oft-used motion estimation approaches is not visually satisfied for those MBs of which adjacent MBs do not have a consistent movement. Therefore, this work will propose and demonstrate that, if the conventional error concealment approach is followed by the nonparametric regression approach GRNN, the concealed quality will be raised. The simulation results will show the proposed approach indeed improves the performance of error concealment and the improving gain is about 1 dB of PSNR.</description><subject>error concealment</subject><subject>Estimation</subject><subject>general regression neural network</subject><subject>Joints</subject><subject>Probabilistic logic</subject><subject>PSNR</subject><subject>Simulation</subject><subject>Training</subject><subject>Visualization</subject><issn>1945-7871</issn><issn>1945-788X</issn><isbn>1612843484</isbn><isbn>9781612843483</isbn><isbn>1612843506</isbn><isbn>9781612843490</isbn><isbn>9781612843506</isbn><isbn>1612843492</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAYheMXOOd-gHiTP9D6pvm-lLLNwpwgCt6NtE0k0qQlq4P9ewuOnZsHzgPn4iD0QCAnBPRTVb4u8wIIyQWQAri6QHdEkEIxykFcohnRjGdSqa-rs2CKXZ-FJLdosd__wBTJmAY6Q2UVhtQffPzGow1Dn0yHbUp9wk0fG2u6YOOI6yNev2-32Ed88K3tJxnCb_SNGX0f79GNM93eLk6co8_V8qN8yTZv66p83mSeSD5mVjoqBbW2dtAYUeiGa-q4cy2bioIWWnLaQg1aaCkccKJAClMzKqQ0QtM5evzf9dba3ZB8MOm4O51B_wCEmk4_</recordid><startdate>201107</startdate><enddate>201107</enddate><creator>Jun-Horng Chen</creator><creator>Shih-Chun Shao</creator><creator>Wen-Hui Chen</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201107</creationdate><title>Improving temporal error concealment by GRNN in video communication</title><author>Jun-Horng Chen ; Shih-Chun Shao ; Wen-Hui Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e7f3763eebf0ca629c593f5ffd4f0c2329753d0b096976f0518076ab43677a693</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>error concealment</topic><topic>Estimation</topic><topic>general regression neural network</topic><topic>Joints</topic><topic>Probabilistic logic</topic><topic>PSNR</topic><topic>Simulation</topic><topic>Training</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Jun-Horng Chen</creatorcontrib><creatorcontrib>Shih-Chun Shao</creatorcontrib><creatorcontrib>Wen-Hui Chen</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jun-Horng Chen</au><au>Shih-Chun Shao</au><au>Wen-Hui Chen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improving temporal error concealment by GRNN in video communication</atitle><btitle>2011 IEEE International Conference on Multimedia and Expo</btitle><stitle>ICME</stitle><date>2011-07</date><risdate>2011</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>1945-7871</issn><eissn>1945-788X</eissn><isbn>1612843484</isbn><isbn>9781612843483</isbn><eisbn>1612843506</eisbn><eisbn>9781612843490</eisbn><eisbn>9781612843506</eisbn><eisbn>1612843492</eisbn><abstract>This work aims to improve the temporal error concealment for the corrupted macroblocks whose motions are not locally-smooth. It is demonstrated that the recovered quality by the oft-used motion estimation approaches is not visually satisfied for those MBs of which adjacent MBs do not have a consistent movement. Therefore, this work will propose and demonstrate that, if the conventional error concealment approach is followed by the nonparametric regression approach GRNN, the concealed quality will be raised. The simulation results will show the proposed approach indeed improves the performance of error concealment and the improving gain is about 1 dB of PSNR.</abstract><pub>IEEE</pub><doi>10.1109/ICME.2011.6012058</doi><tpages>4</tpages></addata></record> |
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| identifier | ISSN: 1945-7871 |
| ispartof | 2011 IEEE International Conference on Multimedia and Expo, 2011, p.1-4 |
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| language | eng |
| recordid | cdi_ieee_primary_6012058 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | error concealment Estimation general regression neural network Joints Probabilistic logic PSNR Simulation Training Visualization |
| title | Improving temporal error concealment by GRNN in video communication |
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