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Proceedings Paper

Adaptive update using visual models for lifting-based motion-compensated temporal filtering
Author(s): Song Li; H. Kai Xiong; Feng Wu; Hong Chen
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Paper Abstract

A fundamental difference in the MCTF coding scheme from the conventional compensated DCT schemes is that the predicted residue is further used to update the temporal low-pass frames. If the motion prediction is inaccurate, it would introduce ghost at the low-pass frames when some high-pass frames are dropped due to limited channel bandwidth or device capability. However, it will definitely hurt the coding efficiency if the update step is removed totally. To solve the dilemma, this paper proposes a content adaptive update scheme, where the JND (Just Noticeable Difference) metric is used to evaluate the impact of the update steps in terms of visual quality at the low-pass frames. The JND thresholds are image dependent, and as long as the update information remains below these thresholds, we achieve “update residual” transparency. Therefore, the potential ghost artifacts detected by the model can be alleviated by adaptively removing visible part of the predicted residues. Experimental results show that the proposed algorithm not only significantly improves subjective visual quality of the temporal low-pass frames but also maintains the PSNR performance compared with the normal full update.

Paper Details

Date Published: 14 March 2005
PDF: 8 pages
Proc. SPIE 5685, Image and Video Communications and Processing 2005, (14 March 2005); doi: 10.1117/12.588064
Show Author Affiliations
Song Li, Shanghai Jiao Tong Univ. (China)
H. Kai Xiong, Shanghai Jiao Tong Univ. (China)
Feng Wu, Microsoft Research Asia (China)
Hong Chen, California State Univ. (United States)


Published in SPIE Proceedings Vol. 5685:
Image and Video Communications and Processing 2005
Amir Said; John G. Apostolopoulos, Editor(s)

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