Adaptive scalar quantization without side information
A. Ortega and M. Vetterli
IEEE Transactions on Image Processing, to appear, 1997
In this paper we introduce a novel technique for adaptive scalar quantization. Adaptivity is useful in applications, including image compression, where the statistics of the source are either not known a priori or will change over time. Our algorithm uses previously quantized samples to estimate the distribution of the source and does not require that side information be sent in order to adapt to changing source statistics. Our quantization scheme is thus backward adaptive. We propose that an adaptive quantizer can be separated into two building blocks, namely, model estimation and quantizer design. The model estimation produces an estimate of the changing source probability density function, which is then used to re-design the quantizer using standard techniques. We introduce non-parametric estimation techniques that only assume smoothness of the input distribution. We discuss the various sources of error in our estimation and argue that for a wide class of sources with a smooth pdf we provide a good approximation to a ``universal'' quantizer, with the approximation becoming better as the rate increases. We study the performance of our scheme and show how the loss due to adaptivity is minimal in typical scenarios. In particular, we provide examples and show how our technique can achieve SNRs within 0.05 dB of the optimal Lloyd-Max quantizer for a memoryless source, while achieving over 1.5 dB gain over a fixed quantizer for a bi-modal source.

Download PDF