Wavelet-based image compression by hierarchical quantization indexing

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Despite its simplicity, the algorithm produces PSNR results that are competitive with the state-of-art coders in literature.