Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, a new video coding paradigm based on the Slepian-Wolf [1] and Wyner-Ziv [2] 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 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.

Distributed Video Coding (DVC) is a new coding paradigm based on two major Information Theory results: the Slepian-Wolf [1] and Wyner-Ziv [2] theorems. DVC theory relies on the coding of two or more dependent random sequences in an independent way, i.e. associating an independent encoder to each sequence. A single decoder is used to perform joint decoding of all encoded sequences exploiting the statistical dependencies between them. Distributed Video Coding allows therefore shifting complexity form the encoder to the decoder since currently, conventional video coding schemes exploit the statistical dependencies at the encoder. Improved error resilience is another major functionality of this new video coding paradigm since the usual encoder prediction loop and the associated error propagation does not exist anymore. These characteristics make of DVC a promising solution to fulfill the requirements of several emerging applications, e.g. wireless low-power surveillance networks, multimedia sensor networks, wireless PC cameras and mobile camera phones, where low encoding complexity is a demand.

The main objective of this Thesis is to study, develop and evaluate new, more efficient algorithms for DVC, thus reducing the gap in performance when compared to the traditional video coding systems. Practical efforts towards distributed video coding solutions are, nowadays, just starting and the technology is not yet sufficiently mature. The available state-of-the-art results, in terms of rate-distortion performance, are promising; however it is essential to improve and to create tools for the DVC scenario with the purpose of achieving better rate-distortion performances than the ones available today in the literature.

[1]  D. Slepian and J. Wolf, “Noiseless Coding of Correlated Information Sources”, IEEE Transactions on Information Theory, vol. 19, no. 4, pp. 471-480, July 1973.

[2]  A. Wyner and J. Ziv, “The Rate-Distortion Function for Source Coding with Side Information at the Decoder”, IEEE Transactions on Information Theory, vol. 22, no. 1, pp. 1-10, January 1976.