Image coding using vector quantization in LP/sub /spl infin// space

Abstract

This paper presents a vector quantization scheme, based on the current understanding of early vision. This allows one to capture more details around the region of interest, and less details farther from that region, which can be achieved by a variable resolution in the feature domain. A cone beam form, nonuniform, multidimensional sampling strategy has been introduced, where the size of each cell increases proportionally to the inverse of the cell's distance from the region of interest. To simplify the actual realization, a discrete-ray-distance has been introduced in the LP/sub /spl infin// space, generating hyper-cubic cells. This approach can be used in MRI image representation, the 3-D Radon transformation, and volumetric image reconstruction.