Wavelet image coding based on significance extraction using morphological operations

An algorithm is proposed for improving Servetto et al.'s method of morpholo gical representation of wavelet data (MRWD), which is among the most effici ent wavelet-based image compression algorithms. In MRWD, morphological dila tion is used to capture and encode the arbitrarily shaped clusters of signi ficant coefficients within each subband and high compression is achieved. B ut there are still several deficiencies for rectification in MRWD. An effic ient image compression algorithm is proposed, in which, for each subband, m orphological dilation is first used to extract and encode the clustered sig nificant coefficients, and the remaining space is encoded in an efficient w ay. Instead of encoding the large number of zeros one by one, only the smal l number of remaining significant coefficients and their positional informa tion are encoded. Experimental results show that this improvement is very e ffective, especially for images with large and relatively smooth regions.