Abstract
In spatial image vector quantization, a vector dimension cannot be made high enough to limit intervector redundancy because of its large computational complexity and storage requirement, so consequently satisfactory compression cannot be achieved. To cope with the foregoing problem, we developed new schemes of spatial image vector quantization that utilize intervector redundancy for compression by employing a self-organizing list of codeword-indices as an auxiliary data structure. We, first, propose two basic schemes : one that encodes codeword-indices by using the list and the other employs the list for encoder's search as well as index coding. We, afterwards, enhance the basic scheme with two different techniques : one that adds a new codeword to an initial codebook and the other gradually builds up a high-dimensional codebook by concatenating low-dimensional code words. The simulation results demonstrate that the proposed schemes successfully exploit intervector redundancy for compression of a sampled image.
