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Robust perceptual coding of overcomplete frame expansions

The cortex transform provides a meaningful representation of images in terms of the responses of cortical cells. It is based on experimental results form human vision research. The multiple orientations obtained in the expansion are of interest for image analysis applications. In image coders, quantization can exploit to a large extent psychovisual properties. This transform belongs to a group of overcomplete transforms. This property has not benefitted their use in coding applications. However, the inherent redundancy of overcomplete representation can be exploited to increase the robustness of the code. Multiple description coding of overcomplete expansions has been reported to confer more graceful degradation to partial reconstructions in the event of channel erasures. This paper proposes a coding strategy based on orientation transforms that yield perceptually meaningful coefficients. The coding budget is reduced by sampling and quantization. The remaining redundancy is used to provide robustness. In addition, the descriptions can be organized to allow progressive reconstruction. The tradeoff between quantization strength, perceived quality, redundancy and robustness can be incorporated in the design of the coder.