5-D Epanechnikov Mixture-of-Experts in Light Field Image Compression
Boning Liu, Yan Fei Zhao, Xiaomeng Jiang, Xingguang Ji, Shigang Wang, Yebin Liu, Jian Jun Wei · IEEE Transactions on Image Processing · 2024
In this study, we propose a modeling-based compression approach for dense/lenslet light field images captured by Plenoptic 2.0 with square microlenses. This method employs the 5-D Epanechnikov Kernel (5-D EK) and its associated theories. Owing to the limitations of modeling larger image block using the Epanechnikov Mixture Regression (EMR), a 5-D Epanechnikov Mixture-of-Experts using Gaussian Initialization (5-D EMoE-GI) is proposed. This approach outperforms 5-D Gaussian Mixture Regression (5-D GMR). The modeling aspect of our coding framework utilizes the entire EI and the 5D Adaptive Model Selection (5-D AMLS) algorithm. The experimental results demonstrate that the decoded rendered images produced by our method are perceptually superior, outperforming High Efficiency Video Coding (HEVC) and JPEG 2000 at a bit depth below 0.06bpp.