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Author:

Lu, Zhuo-Yi (Lu, Zhuo-Yi.) | Jia, Ke-Bin (Jia, Ke-Bin.) (Scholars:贾克斌) | Siu, Wan-Chi (Siu, Wan-Chi.)

Indexed by:

EI Scopus

Abstract:

As a key technique in network multimedia signal processing, video transcod- ing becomes a hot topic in recent years. This paper presents a fast intra mode deci- sion scheme for down-sizing video transcoding in H.264 based on hybrid characteristic of multi-scale videos. In order to reduce the high computational complexity of using con- ventional intra prediction in the H.264 re-encoder, the proposed scheme firstly utilizes 2D-histogram to extract the spatial characteristic of macro-blocks in the downsized video to choose from intra 16×16 and intra 4×4. Then Support Vector Machine (SVM) is used to exploit the correlation between coding information extracted from the input high- resolution bit-stream and the coding modes of macro-blocks in down-sized video frames. After the SVM classifier, improbable modes in the nine intra 4×4 modes are eliminated and only a small number of candidate modes are carried out using the RDO operations. Hence, remarkable computing time can be saved, up to 74%, while maintaining nearly the same quality of the transcoded pictures. © 2012 ISSN 2073-4212.

Keyword:

Image coding Support vector machines Video signal processing Graphic methods

Author Community:

  • [ 1 ] [Lu, Zhuo-Yi]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Lu, Zhuo-Yi]Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong
  • [ 3 ] [Jia, Ke-Bin]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 4 ] [Siu, Wan-Chi]Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong

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Source :

Journal of Information Hiding and Multimedia Signal Processing

ISSN: 2073-4212

Year: 2012

Issue: 1

Volume: 3

Page: 34-46

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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