An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel

Boning Liu; Yan Zhao; Xiaomeng Jiang; Shigang Wang

ICASSP 2019accepted0 citations

An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel

Boning Liu, Yan Zhao, Xiaomeng Jiang, Shigang Wang

Abstract

In this paper, we propose an optimal modeling framework for image compression using EMM (Epanechnikov Mixture Model). Epanechnikov Kernel and its correlated statistics are basement of our Epanechnikov Mixture Regression (EMR). In our scheme, the stochastic processes of the pixel values are modelled as an EMM with K experts in three-dimensional space and then we use EMR to search for the optimal solution, whose parameters are determined through EM (Expectation-Maximization) algorithm. In the process of regression, the conditional density is the regression kernel function. Experimental results show that the proposed scheme is effective especially for the image with complex texture without consuming extra bits compared to Gaussian Mixture Regression (GMR).

BibTeX

@inproceedings{icassp2019_animagecodingapp,
  title = {An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel},
  author = {Boning Liu and Yan Zhao and Xiaomeng Jiang and Shigang Wang},
  booktitle = {ICASSP 2019},
  year = {2019}
}