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240629s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3418350
|2 doi
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|a eng
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| 100 |
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|a Liu, Boning
|e verfasserin
|4 aut
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|a 5-D Epanechnikov Mixture-of-Experts in Light Field Image Compression
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 04.07.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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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|a Journal Article
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|a Zhao, Yan
|e verfasserin
|4 aut
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| 700 |
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|a Jiang, Xiaomeng
|e verfasserin
|4 aut
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| 700 |
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|a Ji, Xingguang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Shigang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Yebin
|e verfasserin
|4 aut
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| 700 |
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|a Wei, Jian
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 30., Seite 4029-4043
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:33
|g year:2024
|g day:30
|g pages:4029-4043
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|u http://dx.doi.org/10.1109/TIP.2024.3418350
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