| Summary: | Advances in communication and compression technologies have facilitated the transmission of high quality video content across a broad range of net- works to numerous terminal types. Challenges for video coding continue to increase due to the demands on bandwidth from increased frame rates, higher resolutions and complex formats. In most cases, the target of any video coding algorithm is, for a given bitrate, to provide the best subjective quality rather than simply produce the most similar pictures to the originals. Based on this premise, texture analysis and synthesis can be utilised to provide higher performance video codecs. This thesis describes a novel means of parametric video compression based on texture warping and synthesis. Instead of encoding whole images or prediction residuals after translational motion estimation, this approach employs a perspective motion model to warp static textures and utilises texture synthesis to create dynamic textures. Texture regions are segmented using features derived from the com- plex wavelet transform and further classified according to their spatial and temporal characteristics. A compatible artefact-based video metric (AVM) has been designed to evaluate the quality of the reconstructed video. Its enhanced version is further developed as a generic perception-based video metric offering improved performance in correlation with subjective opinions. It is unique in being able to assess both synthesised and conventionally coded content. The AVM is accordingly employed in the coding loop to prevent warping and synthesis artefacts, and a local RQO strategy is then developed based on it to make a trade-off between waveform coding and texture warping/synthesis. In addition, these parametric texture models have been integrated into an H.264 video coding framework whose results show significant coding efficiency improvement, up to 60% bitrate savings over H.264/ AVC, on diverse video content.
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