Principal component vector quantization for abrupt scene changes

The authors present a vector quantization technique, referred to as principal component vector quantization (PCVQ), and investigate the problem of abrupt scene changes of video signals. This technique, featuring a simple design procedure, is implemented by an artificial neural network with a learning algorithm for online codebook design based on the local statistics for each difference scene. This network features online learning and a constant encoding time that is independent of the codebook size. The PCVQ was examined via simulation on an abrupt scene change problem which had nonstationary training sequences