A predictive residual VQ using modular neural network vector predictor
Lincheng Wang, Syed A. Rizvi, Nasser M. Nasrabadi · 2002
This paper presents a predictive residual vector quantization (PRVQ) scheme using a modular neural network vector predictor. The proposed PRVQ scheme takes the advantage of the high prediction gain and the improved edge fidelity of a modular neural network vector predictor in order to implement a high performance vector quantization (VQ) scheme with low search complexity and a high perceptual quality. Simulation results show that the proposed PRVQ with modular vector predictor outperforms the equivalent PRVQ with general vector predictor (operating at the same bit rate) by more than 1 dB. Furthermore, the perceptual quality of the reconstructed image is also improved.