Adaptive quantization of image subbands with efficient overhead rate selection

Abstract

Subband image coding techniques owe much of their success to an effective use of adaptive quantization and adaptive entropy coding. It is often the case that adaptive quantization is achieved by defining a discrete set of quantizers from which one is chosen for a given set of coefficients. This type of forward adaptation thus requires that overhead information (the choice of quantizer) be sent to the decoder. Then, the quantized coefficients are transmitted using adaptive entropy coding, typically through backward adaptive arithmetic coding. We show that a combination of forward and backward adaptation methods can be used to update the quantizers thus reducing the overhead requirements while still providing good performance. Specifically, we present an algorithm where each coefficient is classified into several classes based on the past quantized data and where the quantizer to be used for each class can itself be adapted on the fly.