Light Field Compression Using Global Multiplane Representation and Two-Step Prediction

Abstract

Due to its spatio-angular structure, light field image allows for a wealth of post-processing techniques like digital refocusing and depth estimation. In order to compress the data of the two domains, the current proposal intends to embed the disparity-based view synthesis method into the decoder. However, predicting each view separately or in local groups means bringing more computational burden to the decoder and destroying the light field structure. Since disparity contains the relationship between all light rays in the light field, the proposed solution is to predict a disparity-based global representation as the first step. In the second step, all the views can be predicted easily based on this representation. In this letter, we use the recently proposed multiplane as the form of this global representation. The experimental results show the effectiveness of the proposed solution, and the better RD performance compared to other schemes especially under low bitrates.