On the Importance of Source Classification in Wyner-Ziv Video Coding

Source classification has been widely studied in conventional coding of image and video signals. This paper explores the idea of exploiting so-called classification gain in Wyner-Ziv (WZ) video coding. We first provide theoretical analysis of how source classification can lead to improved Rate-Distortion tradeoff in WZ coding and quantify the classification gain by the ratio of weighted arithmetic mean to weighted geometric mean over subsources. Then we present a practical WZ video coding algorithm based on the source classification principle. The statistics of both spatial and temporal correlation are taken into account in our classification strategy. Specifically, the subsource with the steepest R-D slope is identified to be the class of significant wavelet coefficients of the blocks that are poorly motion-compensated in WZ frames. In such classification-based approach, rate control is performed at the decoder which can be viewed as the dual to conventional video coding where R-D optimization stays with the encoder. By combining powerful LDPC codes (for generating coded information) with advanced temporal interpolation (for generating side information), we have observed that the new Wyner-Ziv coder achieves highly encouraging performance for the test sequences used in our experiments. For example, the gap between H264 JM11.0 (I-B-I-B...) and the proposed WZ video coder is dramatically reduced for foreman and hall QCIF sequences when compared with the best reported results in the literature.