A flexible scalable video coding framework with adaptive spatio-temporal decompositions

The work presented in this thesis covers topics that extend the scalability functionalities in video coding and improve the compression performance. Two main novel approaches are presented, each targeting a different part of the scalable video coding (SVC) architecture: motion adaptive wavelet transform based on the wavelet transform in lifting implementation, and a design of a flexible framework for generalised spatio-temporal decomposition. Motion adaptive wavelet transform is based on the newly introduced concept of connectivity-map. The connectivity-map describes the underlying irregular structure of regularly sampled data. To enable a scalable representation of the connectivity-map, the corresponding analysis and synthesis operations have been derived. These are then employed to define a joint wavelet connectivity-map decomposition that serves as an adaptive alternative to the conventional wavelet decomposition. To demonstrate its applicability, the presented decomposition scheme is used in the proposed SVC framework, as a substitute for the spatial transform of the high-pass temporal frames. The proposed novel decomposition framework unifies different SVC architectures, thus providing a flexible platform for adaptive selection of the decomposition path according to the requirements. A concept of the spatio-temporal decomposition tree is introduced, which describes flexible decomposition setting. The corresponding SVC bit-stream is organised in such way that, apart from providing an uneven spatio-temporal plane partition, supports an unequal number of quality layers within each spatio-temporal resolution. Optimisation of performance on the target bit-rates is accomplished by developing a simple statistical model for reconstruction distortion estimation and by applying a post-compression optimisation algorithm on the embedded video bit-stream. The developed framework for scalable video coding provides targeted highly flexible decompositions and therefore can be use in different demanding scenarios. Newly introduced methods for rate-distortion optimisation and adaptive transformation enhance the compression performance and adaptability of the coding scheme to the underlying content.