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Proceedings Paper

Generalized in-scale motion compensation framework for spatial scalable video coding
Author(s): Ruiqin Xiong; Jizheng Xu; Feng Wu; Shipeng Li
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Paper Abstract

In existing video coding schemes with spatial scalability based on pyramid frame representation, such as the ongoing H.264/MPEG-4 SVC (scalable video coding) standard, video frame at a high resolution is mainly predicted either from the lower-resolution image of the same frame or from the temporal neighboring frames at the same resolution. Most of these prediction techniques fail to exploit the two correlations simultaneously and efficiently. This paper extends the in-scale prediction technique developed for wavelet video coding to a generalized in-scale motion compensation framework for H.264/MPEG-4 SVC. In this framework, for a video frame at a high resolution layer, the lowpass content is predicted from the information already coded in lower resolution layer, but the highpass content is predicted by exploiting the neighboring frames at current resolution. In this way, both the cross-resolution correlation and temporal correlation are exploited simultaneously, which leads to much higher efficiency in prediction. Preliminary experimental results demonstrate that the proposed framework improves the spatial scalability performance of current H.264/MPEG-4 SVC. The improvement is significant especially for high-fidelity video coding. In addition, another advantage over wavelet-based in-scale scheme is achieved that the proposed framework can support arbitrary down-sampling and up-sampling filters.

Paper Details

Date Published: 29 January 2007
PDF: 11 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080T (29 January 2007); doi: 10.1117/12.702329
Show Author Affiliations
Ruiqin Xiong, Institute of Computing Technology (China)
Jizheng Xu, Microsoft Research Asia (China)
Feng Wu, Microsoft Research Asia (China)
Shipeng Li, Microsoft Research Asia (China)


Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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