A Transform Domain Classification Based Wyner-Ziv Video Codec
In theory, a Wyner-Ziv video codec should achieve the same efficiency as a joint encoding and decoding one. However, existing approaches still exhibit significant gaps. One main reason is the lack of the complete correlation exploitation between source and side information. In this paper, we propose a transform domain classification based Wyner-Ziv video codec, aiming at exploiting additional video statistics. In this proposed new scheme, the encoder exploits additional statistics by performing block classification to differentiate low motion blocks from high motion ones. In general, low motion blocks represent highly correlated regions. Such information is useful when the decoder performs motion-compensated interpolation to obtain better side information, thus improving the performance of Wyner-Ziv coding. Experimental results show that we are indeed able to achieve better rate-distortion performance compared to the existing Wyner-Ziv video codecs at the expense of some additional complexity in frame store and comparisons at the encoder.