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Image Compression Approach using Segmentation and Total Variation Regularization
In the field of Image Processing, Image segmentation is a low level but important task in entire image understanding system which divides an image into its multiple disjoint regions based on homogeneity. In most of the machine vesion and high level image understanding application this is one of the important steps. Till date different techniques of image segmentation are available and hence There exists a huge survey literature in different approaches of Image Segmentation. Selection of image segmentation technique is highly problem specific. There is no versatile algorithm which is applicable for all kinds of images. Optimization based image segmentation is not explored much which can be applied to reduce complexity of the problem. The aim of the paper is to search for an optimized threshold value for Image Segmentation using Particle Swarm Optimization (PSO) algorithm where fitness function is designed based on entropy of the image.
Image segmentation is the process of subdividing a digital image into its constitute regions such that pixels belong to the same region will be same based on some image property (such as grayscale value, color, texture) and pixels in the different group will be different based on the same image property. Till date, different researchers have taken image segmentation problem from a different point of view and developed several image segmentation algorithms. This paper is going to address an optimization-based approach in color image segmentation where optimized threshold value is chosen by maximizing the Kapur’s Entropy Function.
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.