Wavelet image coding based on significance extraction using morphological operations

Abstract

An algorithm is proposed for improving Servetto et al.'s method of morphological representation of wavelet data (MRWD), which is among the most efficient wavelet-based image compression algorithms. In MRWD, morphological dilation is used to capture and encode the arbitrarily shaped clusters of significant coefficients within each subband and high compression is achieved. But there are still several deficiencies for rectification in MRWD. An efficient image compression algorithm is proposed, in which, for each subband, morphological dilation is first used to extract and encode the clustered significant coefficients, and the remaining space is encoded in an efficient way. Instead of encoding the large number of zeros one by one, only the small number of remaining significant coefficients and their positional information are encoded. Experimental results show that this improvement is very effective, especially for images with large and relatively smooth regions.