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Abstract:

Since demand for image and video content continues to outstrip available bandwidth there is a need for improved image and video compression techniques. However, improvements in traditional approaches are providing diminishing returns and thus new redundancies in visual content must be exploited. In this thesis a new architecture for image compression is proposed in which textures are removed from a source image and replaced using perceptually similar synthesised textures during decoding. Implementations for two codecs are presented, one block-based and open-loop and the other wavelet based with a synthesis quality-assessment feedback loop. In both techniques texture regions are located and the homogeneity assessed. Three homogeneity techniques are described and evaluated. For the texture synthesis process patch-based techniques are used. These are combined with region colour reconstruction techniques of which two are described and assessed. Common synthesis artefacts are determined and then two artefact detection techniques are proposed and discussed. Finally a new texture synthesis technique is described, which builds upon the findings of the prior work, and introduces a patch placement optimisation scheme that incorporates a spatial model of the sample texture to ensure the global structure of the synthesised texture is maintained. Results for the proposed algorithms are given to validate their performance and images coded using the two image compression architectures show savings of up to 26% over JPEG and up to 11% over JPEG2000 at the same quantisation. The perceptual quality of the results is discussed and, where appropriate, improvements suggested and otherwise the limitations highlighted. The results indicate that utilising higher level perceptual redundancies can indeed offer significant benefits to compression although more research is needed from both the fields of the psychology of vision and image processing.