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Proceedings Paper

Adaptive filtering for cross-view prediction in multi-view video coding
Author(s): PoLin Lai; Yeping Su; Peng Yin; Cristina Gomila; Antonio Ortega
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Paper Abstract

We consider the problem of coding multi-view video that exhibits mismatches in frames from different views. Such mismatches could be caused by heterogeneous cameras and/or different shooting positions of the cameras. In particular, we consider focus mismatches across views, i.e., such that different portions of a video frame can undergo different blurriness/sharpness changes with respect to the corresponding areas in frames from the other views. We propose an adaptive filtering approach for cross-view prediction in multi-view video coding. The disparity fields are exploited as an estimation of scene depth. An Expectation-maximization (EM) algorithm is applied to classify the disparity vectors into groups. Based on the classification result, a video frame is partitioned into regions with different scene-depth levels. Finally, for each scene-depth level, a two-dimensional filter is designed to minimize the average residual energy of cross-view prediction for all blocks in the class. The resulting filters are applied to the reference frames to generate better matches for cross-view prediction. Simulation results show that, when encoding across views, the proposed method achieves up to 0.8dB gain over current H.264 video coding.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 650814 (29 January 2007); doi: 10.1117/12.707437
Show Author Affiliations
PoLin Lai, Thomson Corporate Research (United States)
Univ. of Southern California (United States)
Yeping Su, Thomson Corporate Research (United States)
Peng Yin, Thomson Corporate Research (United States)
Cristina Gomila, Thomson Corporate Research (United States)
Antonio Ortega, Univ. of Southern California (United States)

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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