Coding of Wavelet - Transformed Images

Compression methods are widely used for reducing storage and enhanc-ing transfer of digital images. By selectively discarding visually subtle detailsfrom images, it is possible to represent images with only a fraction of the bitsrequired for the uncompressed images. The best lossy image compression meth-ods currently used are based on quantization, modeling and entropy coding oftransformed images.This thesis studies quantization and modeling of wavelet-transformed nat-ural images. Usage of different quantization levels for the different regions ofthe image is discussed and a new variable quality image compression method isintroduced. The benefits of the variable quality image coding are demonstratedfor coding of mammography images. The quantization of the transform coef-ficients is controlled in most of the lossy image coding algorithms by setting alimit to the size of the compressed image or by directly defining the magnitudeof the quantifier. It is shown here how the distortion in the decompressed imagecan be used as the quantization criterion and a new image coding algorithmthat implements this criterion is introduced.While a wavelet transformed image is encoded, both the coder and decoderknow the values of the already encoded coefficients. The thesis studies how thiscoding context can be used for compression. It is shown that conventional pre-diction methods and scalar quantization can be used for modeling coefficientsand introduce a new coding algorithm that predicts the number of significantbits in the coefficients from their context. A general method of adaptively mod-eling probability distributions of encoded coefficients from a property vectorcalculated from the coefficient context is given. This method is based on vectorquantization of the property vectors and achieves excellent compression perfor-mance. Forming high quality code books for vector quantization is studied.Self-adaptive genetic algorithms are used for the optimization of the code booksand a new model for parallelization of the algorithm is introduced. The modelallows efficient distribution of the optimization problem to multiple networkedprocessors and flexible reconfiguration of the network topology.