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Image compression based on Gaussian mixture model constrained using Markov random field
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DOI：10.1016/j.sigpro.2021.107990.png)
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Abstract
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We introduce a Gaussian Mixture Model (GMM) constrained by Markov Random Field (MRF) framework for image compression in this paper. The image is predicted using GMM with MRF and the parameters of the GMM are estimated using an adjusted Expectation-Maximization (EM) algorithm. Mixture Model Optimization (MMO) is used in this framework to select the optimal number of distributions and avoid local optimum of EM at the same time. Parameters are encoded using fixed-length bits. A codebook is used to improve the coding efficiency of the covariance parameters. The residual between the original image and the prediction is encoded using High Efficiency Video Coding (HEVC) intra coding. Experimental results show that our method performs better than our previous work, HEVC, JPEG 200 0 and Better Portable Graphics (BPG) which is an improved version of HEVC. (C) 2021 The Authors. Published by Elsevier B.V.
Keywords:
Gaussian mixture model
Markov random field
Expectation-maximization
Mixture model optimization
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