Knowledge based image sequence compression

In this thesis, most commonly encountered video compression techniques and international coding standards are studied. The study leads to the idea of a reconfigurable codec which can adapt itself to the specific requirements of diverse applications so as to achieve improved performance. Firstly, we propose a multiple layer affine motion compensated codec which acts as a basic building block of the reconfigurable multiple tool video codec. A detailed investigation of the properties of the proposed codec is carried out. The experimental results reveal that the gain in coding efficiency from improved motion prediction and segmentation is proportional to the spatial complexity of the sequence being encoded. Secondly, a framework for the reconfigurable multiple tool video codec is developed and its key parts are discussed in detail. Two important concepts virtual codec and virtual tool are introduced. A prototype of the proposed reconfigurable multiple tool video codec is implemented. The codec structure and the constituent tools of the codec included in the prototype are extensively tested and evaluated to prove the concept. The results confirm that different applications require different codec configurations to achieve optimum performance. Thirdly, a knowledge based tool selection system for the reconfigurable codec is proposed and developed. Human knowledge as well as sequence properties are taken into account in the tool selection procedure. It is shown that the proposed tool selection mechanism gives promising results. Finally, concluding remarks are offered and future research directions are suggested.

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