1 Introduction

Wavelet still image compression has recently been a focus of intense research, and appears to be maturing as a subject. Considerable coding gains over older DCT-based methods have been achieved, although for the most part the computational complexity has not been very competitive. We report here on a high-performance wavelet still image compression algorithm optimized for either mean-squared error (MSE) or human visual system (HVS) characteristics, but which has furthermore been streamlined for extremely efficient execution in high-level software. Ideally, all three components of a typical image compression system: transform, quantization, and entropy coding, should be optimized simultaneously. However, the highly nonlinear nature of quantization and encoding complicates the formulation of the total cost function. We present the problem of (sub)optimal quantization from a Lagrange multiplier point of view, and derive novel HVS-type solutions. In this chapter, we consider optimizing the filter, and then the quantizer, separately, holding the other two components fixed. While optimal bit allocation has been treated in the literature, we specifically address the issue of setting the quantization stepsizes, which in practice is slightly different. In this chapter, we select a short high-performance filter, develop an efficient scalar MSE-quantizer, and four HVS-motivated quantizers which add value visually while sustaining negligible MSE losses. A combination of run-length and streamlined arithmetic coding is fixed in this study. This chapter builds on the previous tutorial material to develop a baseline compression algorithm that delivers high performance at rock-bottom complexity. As an example, the 512 512...