Adaptive filtering for cross-view prediction in multi-view video coding

PoLin Lai; Yeping Su; Peng Yin; Cristina Gomila; Antonio Ortega

doi:10.1117/12.707437

29 January 2007 Adaptive filtering for cross-view prediction in multi-view video coding

PoLin Lai, Yeping Su, Peng Yin, Cristina Gomila, Antonio Ortega

Proceedings Volume 6508, Visual Communications and Image Processing 2007; 650814 (2007) https://doi.org/10.1117/12.707437
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

PoLin Lai, Yeping Su, Peng Yin, Cristina Gomila, and Antonio Ortega "Adaptive filtering for cross-view prediction in multi-view video coding", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 650814 (29 January 2007); https://doi.org/10.1117/12.707437

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