A motion compensated vector quantizer with filtered prediction

It is shown that motion compensated vector quantizer (MCVQ) performance can be improved by inserting spatial filters within the MCVQs prediction loop. The degree of improvement depends on the type of filter applied. Median filtering is poorly matched to the MCVQ noise process and does not work well. Gaussian filters do a much better job of cleaning foreground regions; peak SNR improves from 1 to 3 dB, with an average of about 1.5 dB. Edge rendition can be further improved by using an adaptive space-variant filter that smooths in a direction parallel to strong edges. The rate increases slightly to communicate edge orientations, but this information can be used to upgrade the VQ to a classified VQ structure