Entropy-Constrained High-Resolution Lattice Vector Quantization using a Perceptually Relevant Distortion Measure

In this paper we study high-resolution entropy-constrained coding using multidimensional companding. To account for auditory perception, we introduce a perceptual relevant distortion measure. We will derive a multidimensional companding function which is asymptotically optimal in the sense that the rate loss introduced by the compander will vanish with increasing vector dimension. We compare the companding scheme to a scheme which is based on a perceptual weighting of the source, thereby transforming the perceptual distortion measure into a mean-squared error distortion measure. Experimental results show that even at low vector dimension, the rate loss introduced by the compander is low (less than 0.05 bit per dimension in case of two-dimensional vectors).