TABLE OF CONTENTS Since the introduction of wavelets as a signal processing tool in the late 1980s, considerable attention has focused on the application of wavelets to image compression [1], [2], [3], [4], [5], [6]. The hierarchical signal representation given by the dyadic wavelet transform provides a convenient framework both for exploiting the specific types of statistical dependencies found in images, and for designing quantization strategies matched to characteristics of the human visual system. Indeed, before the introduction of wavelets, a wide variety of closely related coding frameworks had been extensively studied in the image coding community, including pyramidal coding [7], transform coding [8] and subband coding [9]. Viewed in the context of this prior work, initial efforts in wavelet coding research concentrated on the promise of more effective compaction of energy into a small number of low frequency coefficients. Following the design methodology of earlier transform and subband coding algorithms, initial "wavelet-based" coding algorithms [3], [4], [5], [6] were designed to exploit the energy compaction properties of the wavelet transform by applying quantizers (either scalar or vector) optimized for the statistics of each frequency band of wavelet coefficients. Such algorithms have demonstrated modest improvements in coding efficiency over standard transform-based algorithms.
Contrasting with those early coders, this paper proposes to exploit both the frequency and spatial compaction property of the wavelet transform through the use of two very simple quantization...
