Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding

Abstract

In recent years, practical Wyner-Ziv (WZ) video coding solutions have been proposed with promising results. Most of the solutions available in the literature model the correlation noise (CN) between the original frame and its estimation made at the decoder, which is the so-called side information (SI), by a given distribution whose relevant parameters are estimated using an offline process, assuming that the SI is available at the encoder or the originals are available at the decoder. The major goal of this paper is to propose a more realistic WZ video coding approach by performing online estimation of the CN model parameters at the decoder, for pixel and transform domain WZ video codecs. In this context, several new techniques are proposed based on metrics which explore the temporal correlation between frames with different levels of granularity. For pixel-domain WZ (PDWZ) video coding, three levels of granularity are proposed: frame, block, and pixel levels. For transform-domain WZ (TDWZ) video coding, DCT bands and coefficients are the two granularity levels proposed. The higher the estimation granularity is, the better the rate-distortion performance is since the deeper the adaptation of the decoding process is to the video statistical characteristics, which means that the pixel and coefficient levels are the best performing for PDWZ and TDWZ solutions, respectively.