High performance wavelet image compression optimized for MSE and HVS metrics

Summary form only. Wavelet still image compression has been a focus of intense research. Considerable coding gains over older DCT-based methods have been achieved, while the computational complexity has been made very competitive. We report on a high performance wavelet still image compression algorithm optimized for both mean-squared error (MSE) and human visual system (HVS) characteristics. We present the problem of optimal quantization from a Lagrange multiplier point of view, and derive novel solutions. 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 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 step sizes, which in practice is quite different. We select a short high-performance filter, develop an efficient scalar MSE-quantizer, and four HVS-motivated quantizers which add some value visually without incurring any MSE losses. A combination of run-length and empirically optimized Huffman coding is fixed in this study