Image Compression Using GMM Model Optimization
Jianjun Sun, Yan Zhao, Shigang Wang
Abstract
A Gaussian Mixture Model (GMM)-based framework for image compression is proposed in this paper. The image is predicted using GMM whose parameters are estimated using common Expectation-Maximization (EM) algorithm and encoded with fixed length bits. We introduce a new GMM Model Optimization (GMO) measure to select the optimal number of models and avoid local optimum of EM at the same time. The encoding cost of the residual and parameters are considered in GMO which is demonstrated to be near concave and effective. A parameter dictionary is designed to utilize the correlation of the parameters to improve the coding efficiency. The residual between the original image and the GMM image is encoded using High Efficiency Video Coding (HEVC) intra coding. Experimental results show that our method performs better than HEVC.
BibTeX
@inproceedings{icassp2019_imagecompression,
title = {Image Compression Using GMM Model Optimization},
author = {Jianjun Sun and Yan Zhao and Shigang Wang},
booktitle = {ICASSP 2019},
year = {2019}
}