Learning dictionaries for matching pursuits based video coders
We present a learning scheme for designing dictionaries of two-dimensional functions for matching pursuits (MP) based video coding. The motivation is to improve the performance of such codecs by adapting the structure of the dictionary functions to specific bit-rates or types of sequences. The scheme we propose is based on vector quantization (VQ), and uses an inner-product based distortion measure. The different processing steps, consisting of data extraction from the motion compensated error frames, training, pruning, and testing, are presented in detail. We find that for high bit-rate QCIF sequences we can achieve improvements of up to 0.66 dB.