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- aggregation classification "P1".
- aggregation creator B35841.
- aggregation creator B35842.
- aggregation creator B35843.
- aggregation creator B35844.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 5821605.bibtex.
- aggregation hasFormat 5821605.csv.
- aggregation hasFormat 5821605.dc.
- aggregation hasFormat 5821605.didl.
- aggregation hasFormat 5821605.doc.
- aggregation hasFormat 5821605.json.
- aggregation hasFormat 5821605.mets.
- aggregation hasFormat 5821605.mods.
- aggregation hasFormat 5821605.rdf.
- aggregation hasFormat 5821605.ris.
- aggregation hasFormat 5821605.txt.
- aggregation hasFormat 5821605.xls.
- aggregation hasFormat 5821605.yaml.
- aggregation isPartOf urn:isbn:9781614991052.
- aggregation isPartOf urn:issn:0922-6389.
- aggregation language "eng".
- aggregation publisher "IOS PRESS".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "H.264/AVC-to-SVC temporal transcoding using machine learning".
- aggregation abstract "Nowadays, networks and terminals with diverse characteristics of bandwidth and capabilities coexist. To ensure a good quality of experience, this diverse environment demands adaptability of the video stream. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a technique to convert an H.264/AVC bitstream without scalability to a scalable bitstream with temporal scalability in Main Profile by accelerating the mode decision task of the SVC encoding stage using Machine Learning tools. The results show that when our technique is applied, the complexity is reduced by 87% while maintaining coding efficiency.".
- aggregation authorList BK90880.
- aggregation endPage "1702".
- aggregation startPage "1693".
- aggregation volume "243".
- aggregation aggregates 5822062.
- aggregation isDescribedBy 5821605.
- aggregation similarTo 978-1-61499-105-2-1693.
- aggregation similarTo LU-5821605.