Entropy-coded pyramid vector quantisation for interband wavelet image coding

Novel class-based entropy coding algorithms for lattice quantised hierarchi cal (or interband) vectors are presented. The vectors an formed from wavele t coefficients on different scales from similarly oriented sub-bands corres ponding to the same spatial location. Structures have been designed specifi cally for interband vectors drawn from wavelet coefficients, where large gr oups of approximately equiprobable lattice points are grouped into relative ly few classes, known as sub-classes, super-classes and super-super-classes , enabling accurate probability estimates from training data to be obtained for entropy coding of the class indices. Further, it has been found that t he best quantiser is a combination of the Z(n) and D-n lattices which has b een termed an augmented lattice. The performance of the method, entitled en tropy-coded pyramid vector quantisation (ECPVQ), is evaluated on real image s and the results show that ECPVQ is competitive, particularly at low bit-r ates, with current state-of-the-art wavelet-based coders. A subjective comp arison with current high performance scalar quantisation based coders shows that ECPVQ is likely to better preserve fine texture detail in the decoded images because of the finer quantisation of low energy wavelet coefficient s that occurs with the augmented lattice.