Learned Focused Plenoptic Image Compression With Local-Global Correlation Learning

Gaosheng Liu, Huanjing Yue, Bihan Wen, Jing-Yu Yang

Abstract: The dense light field sampling of focused plenoptic images (FPIs) yields substantial amounts of redundant data, necessitating efficient compression in practical applications. However, the presence of discontinuous structures and long-distance properties in FPIs poses a challenge. In this paper, we propose a novel end-to-end approach for learned focused plenoptic image compression (LFPIC). Specifically, we introduce a local-global correlation learning strategy to build the nonlinear transforms. This strategy can effectively handle the discontinuous structures and leverage long-distance correlations in FPI for high compression efficiency. Additionally, we propose a spatial-wise context model tailored for LFPIC to help emphasize the most related symbols during coding and further enhance the rate-distortion performance. Experimental results demonstrate the effectiveness of our proposed method, achieving a 22.16% BD-rate reduction (measured in PSNR) on the public dataset compared to the recent state-of-the-art LFPIC method. This improvement holds significant promise for benefiting the applications of focused plenoptic cameras.