Exploiting Correlation Noise Modeling in Wyner-Ziv Video Coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, a new video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems which mainly exploit the source correlation at the decoder and not only at the encoder as in predictive video coding. Therefore, this new coding paradigm may provide a flexible allocation of complexity between the encoder and the decoder and in-built channel error robustness, interesting features for emerging applications such as low-power video surveillance and visual sensor networks among others. Although some progress has been made in the last eight years, the rate-distortion performance of WZ video coding is still far from the maximum performance attained with predictive video coding. The WZ video coding compression efficiency depends critically on the capability to model the correlation noise between the original information at the encoder and its estimation generated at the decoder, known as side information. The development of realistic and powerful correlation noise modeling techniques is, therefore, crucial to reach practical and efficient WZ video coding solutions. In addition, to also address application scenarios where a feedback channel is not available, it is necessary to develop encoder driven rate control strategies. In this context, this Thesis proposes: 1) several new techniques to efficiently model, at the decoder, the correlation noise between the original frame and the corresponding side information, for pixel and transform domain WZ video decoding; 2) an efficient encoder rate control strategy for a transform domain WZ video coding architecture, initially using a feedback channel driven rate control mechanism; and 3) a complete and meaningful performance assessment of advanced decoder and encoder rate control based transform domain WZ video coding architectures. Overall, the results obtained show that the proposed techniques lead to efficient and competitive WZ video coding solutions.