Rate-constrained modular predictive residual vector quantization

Abstract

This paper investigates a novel modular image coding paradigm using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks, where each expert network is optimized for predicting a particular class of data, and an integrating unit that mixes the outputs of the expert networks in order to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (MPRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same PSNR, the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.