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

Liu, Boning; Zhao, Yan; Jiang, Xiaomeng; Wang, Shigang

doi:10.1109/icassp.2019.8682374

Cited by 6 publications

(1 citation statement)

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“…In our previous work, we had developed a rudimentary study on image coding based on EK, which lacked a complete theory and coding scheme design [20] . In this paper, we utilize the theory of mathematical statistics and kernel density estimation to obtain a complete derivation of EK and its related statistics.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Epanechnikov mixture regression in image coding

et al. 2021

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Kernel methods have been studied extensively in recent years. We propose a three-dimensional (3-D) Epanechnikov Mixture Regression (EMR) based on our Epanechnikov Kernel (EK) and realize a complete framework for image coding. In our research, we deduce the covariance-matrix form of 3-D Epanechnikov kernels and their correlated statistics to obtain the Epanechnikov mixture models. To apply our theories to image coding, we propose the 3-D EMR which can better model an image in smaller blocks compared with the conventional Gaussian Mixture Regression (GMR). The regressions are all based on our improved Expectation-Maximization (EM) algorithm with mean square error optimization. Finally, we design an Adaptive Mode Selection (AMS) algorithm to realize the best model pattern combination for coding. Our recovered image has clear outlines and superior coding efficiency compared to JPEG below 0.25bpp. Our work realizes an unprecedented theory application by: (1) enriching the theory of Epanechnikov kernel, (2) improving the EM algorithm using MSE optimization, (3) exploiting the EMR and its application in image coding, and (4) AMS optimal modeling combined with Gaussian and Epanechnikov kernel.

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Section: Introductionmentioning

confidence: 99%