5-D Epanechnikov Mixture-of-Experts in Light Field Image Compression

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...

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Veröffentlicht in:IEEE transactions on image processing Jg. 33; S. 4029 - 4043
Hauptverfasser: Liu, Boning, Zhao, Yan, Jiang, Xiaomeng, Ji, Xingguang, Wang, Shigang, Liu, Yebin, Wei, Jian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
5-D Epanechnikov kernel
5-D Epanechnikov mixture-of-experts
Adaptation models
Algorithms
Cameras
Image coding
Image compression
image modeling
Kernel
kernel method
light field image compression
Mathematical models
Microlenses
Mixtures
Modelling
Prediction algorithms
Transform coding
Video compression
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2024.3418350