JND measurements and wavelet based image coding

Two major issues in image coding are the effective incorporation of human visual system (HVS) properties and the effective objective measure for evaluating image quality (OQM). In this paper, we treat the two issues in an integrated fashion. We build a JND model based on the measurements of the JND (Just Noticeable Difference) property of HVS. We found that JND does not only depend on the background intensity but also a function of both spatial frequency and pattern direction. Wavelet transform, due to its excellent simultaneous Time (space)/frequency resolution, is the best choice to apply the JND model. We mathematically derive an OQM called JND_PSNR that is based on the JND property and wavelet decomposed subbands. JND_PSNR is more consistent with human perception and is recommend as an alternative to the PSNR or SNR. With the JND_PSNR in mind, we proceed to propose a wavelet and JND based codec called JZW. JZW quantizes coefficients in each subband with proper step size according to the subband's importance to human perception. Many characteristics of JZW are discussed, its performance evaluated and compared with other famous algorithm such as EZW, SPIHT and TCCVQ. Our algorithm has 1 similar to 1.5 dB gain over SPIHT even we use simple huffman coding rather than the more efficient adaptive arithmetic coding.