Maximum gain block classification in subband coding of images

Image compression is now essential for applications such as transmission and storage in visual databases. Subband or wavelet transform coding has been proved to be an efficient method of image compression. To exploit the non-stationary nature of image subbands, some image compression techniques have been proposed based on the blockwise classification of image subbands. An advantage of this kind of technique is characterized in a rate-distortion framework in terms of a classification gain. Thus, reaching a maximum classification gain becomes a problem with this kind of technique. To this end, an optimum classification algorithm has been introduced. However, it could not guarantee a maximum classification gain. In this paper, we propose an embedded 1D optimization algorithm to classify blocks of image subbands with a maximum classification gain so as to improve the performance of the blockwise classification image coding