ADAPTIVE SCALAR QUANTIZATION WITHOUT SIDE INFORMATION

In this paper, we introduce a novel technique for adaptive scalar quan tization, Adaptivity is useful in applications, including image compre ssion, where the statistics of the source are either not known a prior i or will change over time, Our algorithm uses previously quantized sa mples to estimate the distribution of the source, and does not require that side information be sent in order to adapt to changing source st atistics, Our quantization scheme is thus backward adaptive, We propos e 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 functi on, which is then used to redesign the quantizer using standard techni ques, We introduce nonparametric estimation techniques that only assum e smoothness of the input distribution, We discuss the various sources of error in our estimation and argue that, for a wide class of source s with :! smooth probability density function (pdf), we provide a good approximation to a ''universal'' quantizer, with the approximation be coming better as the rate increases, We study the performance of our s cheme and show how the loss due to adaptivity is minimal in typical sc enarios, In particular, we provide examples and show how our technique can achieve signal-to-noise ratios (SNR's) within 0.05 dB of the opti mal Lloyd-Max quantizer (LMQ) for a memoryless source, while achieving over 1.5 dB gain over a fixed quantizer for a bimodal source.