Abstract—In this paper we propose an image coding approach based on Alternative Fuzzy c-Means. Our main objective is to provide an immediate access to targeted features of interest in a high quality decoded image. This technique is useful for intelligent devices, as well as for multimedia content-based description standards. The use of AFcM reduces the coding time in comparison to the traditional clustering algorithm FcM. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decoding phase, we suggest the application of a nonlinear anisotropic diffusion, based on Perona-Malik equation, to enhance the quality of the coded image. Qualitative evaluation confirms the validity of the proposed approach.
Index Terms—Anisotropic non-linear diffusion, entropy coding, fuzzy segmentation, image compression.
The authors are with the Laboratory of Mathematics and Applications, Doctoral School of Sciences and Technology, the Lebanese University, Mitein Street, face to Malaab High School, Tripoli, Lebanon (e-mails: ashahin@ul.edu.lb, fchakik@ul.edu.lb ).
Cite: Ahmad Shahin, Fadi Chakik, and Safaa Al-Ali, "Complexity Reduction and Quality Enhancement in Image Coding," International Journal of Future Computer and Communication vol. 2, no. 3 pp. 205-209, 2013.
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