A comparison of model-based VQ compression with other VQ approaches

In our previous work on Model-Based Vector Quantization (MVQ), we presented some performance comparisons (both rate distortion and decompression time) with VQ and JPED/DCT. In this paper, we compare the MVQ's rate distortion performance with Mean Removed Vector Quantization (MRVQ) and include our previous comparison with VQ. Both MVQ and MRVQ compute the mean of each vector (raster-scanned image block) and produce mean removed residual vectors by subtracting the block mean from the elements of each vector. In the case of MRVQ, a codebook of residual vectors is generated using a training set. For MVQ, an internal codebook is generated based on the statistical properties of the residual vectors, and upon correlations derived from a Human Visual System (HVS) model. In both MVQ and MRVQ, the block mean and address of the codevector from the codebook that most closely matches each input vector are transmitted to the decoder. MVQ, a single additional statistical parameter is transmitted to the decoder. For MRVQ, we assume that the codebook of residual vectors is available to the decoder. In our experiments, we found that the rate of distortion performance of MVQ is almost always better than VQ, and is comparable to MRVQ. Further, MVQ is much easier to use than either VQ or MRVQ, since the training and management of codebooks is not required.