Zoomable Intra Prediction for Multi-Focus Plenoptic 2.0 Video Coding

Abstract

Plenoptic 2.0 videos that record time-varying light fields by focused plenoptic cameras are promising to immersive visual applications because of capturing dense sampled light fields with high spatial resolution in the rendered sub-apertures. In this paper, an intra prediction method is proposed for compressing multi-focus plenoptic 2.0 videos efficiently. Based on the imaging principle analysis of multi-focus plenoptic cameras, zooming relationships among the microimages are discovered and exploited by the proposed method. Positions of the prediction candidates and the zooming factors are derived, after which block zooming and tailoring are proposed to generate novel prediction candidates for weighted prediction. Experimental results demonstrated the superior performance of the proposed method relative to HEVC and state-of-the-art methods.