Stack-run coding with space-frequency quantization

The choice of quantization method in wavelet image compression is a crucial issue that affects performance, quality, and system design. Space-frequency scalar quantization of zero-trees achieves excellent coding efficiency. Stack-run coding is an efficient alternative to zero-trees which maintains independence between subbands. We present a new approach to wavelet quantization which enhances the stack-run coding method. Low addressing complexity, independence between subbands, and fast, parallel decoding are preserved while superior performance is obtained. The most important features are the optimization of dead-zone scalar quantizers, raster scan pattern selection, and the local (spatial) optimization of quantization coefficients. The local optimization is not spatially restricted (as with zero-trees) and new non-recursive optimal algorithms are now possible. Simulation results indicate that the new technique is strongly PSNR competitive with the best of current lossy wavelet image coders. The new framework also allows insight into the nature of the performance gains achieved by space-frequency quantization