Image Compression Approach using Segmentation and Total Variation Regularization

Author:

Shahin Ahmad1,Moudani Walid1,Chakik Fadi1

Affiliation:

1. Doctoral School for Science and Technologies Lebanese University Tripoli, Lebanon

Abstract

In this paper we present a hybrid model for image compression based on segmentation and total variation regularization. The main motivation behind our approach is to offer decode image with immediate access to objects/features of interest. We are targeting high quality decoded image in order to be useful on smart devices, for analysis purpose, as well as for multimedia content-based description standards. The image is approximated as a set of uniform regions: The technique will assign well-defined members to homogenous regions in order to achieve image segmentation. The Adaptive fuzzy c-means (AFcM) is a guide to cluster image data. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decompression phase, the reverse process is applied in which the decoded image suffers from missing details due to the coarse segmentation. For this reason, we suggest the application of total variation (TV) regularization, such as the Rudin-Osher-Fatemi (ROF) model, to enhance the quality of the coded image. Our experimental results had shown that ROF may increase the PSNR and hence offer better quality for a set of benchmark grayscale images.

Publisher

North Atlantic University Union (NAUN)

Reference15 articles.

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5. Martin Welk, David Theis, Thomas Brox, and Joachim Weickert, PDEBased Deconvolution with Forward-Backward Diffusivities and Diffusion Tensors, in Scale Space 2005, Springer LNCS 3459, Hofgeismar, Germany, Apr. 2005, pp. 585-597.

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