Focal Stack Based Light Field Coding for Refocusing Applications Since a light field camera generates huge volume of image data, it requires powerful compression for efficient transmission and storage of its data. In its refocusing applications, users want to see their objects of interest at different depth levels. To address this, we first render a focal stack that contains multifocus images, then compress it instead of original light field data. By doing so, we can minimize the amount of data to process for its target refocusing applications. Our experiment results show that the proposed method can save 54.09% of bitrate and have PSNR gain of 2.08 dB at the same bitrate.

A low complexity WynerZiv coding solution for Light Field image transmission and storage Compressing Light Field (LF) imaging data is a challenging but very important task for both LF image transmission and storage applications. In this paper, we propose a novel coding solution for LF images using the wellknown WynerZiv (WZ) information theorem. First, the LF image is decomposed into a fourthdimensional LF(4DLF) data format. Using a spiral scanning procedure, a pseudosequence of 4DLF is generated. This sequence is then compressed in a distributed coding manner as specified in the WZ theorem. Secondly, a novel adaptive frame skipping algorithm is introduced to further explore the high correlation between 4DLF pseudo sequences. Experimental results show that the proposed LF image compression solution is able to achieve a significant performance improvement with respect to the standard, notably around 54% bitrate saving when compared with the standard High Efficiency Video Coding (HEVC) Intra benchmark while requiring less computational complexity.