Embedded zerotree wavelets coding based on adaptive fuzzy clustering for image compression
In this paper, a new quantization approach based on an adaptive fuzzy c-means clustering for image compression is presented. The fuzzy cluster theory is applied to quantizing the wavelet coefficients of low-frequency subband after the image has been decomposed by wavelet transform. The method can automatically label the importance degree of coefficients of wavelets, and get new constraints on membership condition by weighted average method of the importance and 1 (q(k) = theta((1))(k). 1 + theta((2))(k) . lambda(k), theta((1))(k) + theta((2))(k) = 1). Based on this condition, we cluster again. The proof of convergence of the algorithm is given. The experimental results show that exacter reconstructed values of wavelet coefficients can be obtained at low bit-rates, the subjective and objective quality of the reconstructed image is improved. This technique is shown to yield PSNR of reconstructed images improvement from 0.2 dB to 2.8 dB. This paper has brought about some new ideas in combining the fuzzy cluster algorithm with the embedded zerotree wavelets algorithm. (c) 2007 Elsevier B.V. All rights reserved.
embedded zerotree wavelets coding image compression fuzzy c-means clustering