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 2000 and Better Portable Graphics (BPG) which is an improved version of HEVC.

本文介绍了一种受马尔可夫随机场（MRF）框架约束的高斯混合模型（GMM），用于图像压缩。使用带有MRF的GMM预测图像，并使用调整后的Expectation-Maximization（EM）算法估计GMM的参数。在此框架中使用混合模型优化（MMO）来选择最佳分布数量，同时避免EM的局部最优。使用固定长度的位对参数进行编码。使用码本来提高协方差参数的编码效率。原始图像和预测之间的残差使用高效视频编码（HEVC）帧内编码进行编码。实验结果表明，我们的方法比以前的工作（HEVC，JPEG 2000和更好的便携式图形（BPG））表现更好，后者是HEVC的改进版本。