Multiterminal Video Coding: From Theory to Application

Multiterminal (MT) video coding is a practical application of the MT source coding theory. For MT source coding theory, two problems associated with achievable rate regions are well investigated into in this thesis: a new sufficient condition for BT sum-rate tightness, and the sum-rate loss for quadratic Gaussian MT source coding. Practical code design for ideal Gaussian sources with quadratic distortion measure is also achieved for cases more than two sources with minor rate loss compared to theoretical limits. However, when the theory is applied to practical applications, the performance of MT video coding has been unsatisfactory due to the difficulty to explore the correlation between different camera views. In this dissertation, we present an MT video coding scheme under the H.264/AVC framework. In this scheme, depth camera information can be optionally sent to the decoder separately as another source sequence. With the help of depth information at the decoder end, inter-view correlation can be largely improved and thus so is the compression performance. With the depth information, joint estimation from decoded frames and side information at the decoder also becomes available to improve the quality of reconstructed video frames. Experimental result shows that compared to separate encoding, up to 9.53% of the bit rate can be saved by the proposed MT scheme using decoder depth information, while up to 5.65% can be saved by the scheme without depth camera information. Comparisons to joint video coding schemes are also provided

Remote Source Coding under Gaussian Noise : Dueling Roles of Power and Entropy Power

The distributed remote source coding (so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The main result is a new lower bound for the sum-rate-distortion function under arbitrary distortion measures. When specialized to the case of mean-squared error, it is shown that the bound exactly mirrors a corresponding upper bound, except that the upper bound has the source power (variance) whereas the lower bound has the source entropy power. Bounds exhibiting this pleasing duality of power and entropy power have been well known for direct and centralized source coding since Shannon's work. While the bounds hold generally, their value is most pronounced when interpreted as a function of the number of agents in the CEO problem