Hajisharif, SaghiMiandji, EhsanLarsson, PerTran, KietUnger, JonasLee, Jehee and Theobalt, Christian and Wetzstein, Gordon2019-10-142019-10-1420191467-8659https://doi.org/10.1111/cgf.13835https://diglib.eg.org:443/handle/10.1111/cgf13835Light field imaging is rapidly becoming an established method for generating flexible image based description of scene appearances. Compared to classical 2D imaging techniques, the angular information included in light fields enables effects such as post-capture refocusing and the exploration of the scene from different vantage points. In this paper, we describe a novel GPU pipeline for compression and real-time rendering of light field videos with full parallax. To achieve this, we employ a dictionary learning approach and train an ensemble of dictionaries capable of efficiently representing light field video data using highly sparse coefficient sets. A novel, key element in our representation is that we simultaneously compress both image data (pixel colors) and the auxiliary information (depth, disparity, or optical flow) required for view interpolation. During playback, the coefficients are streamed to the GPU where the light field and the auxiliary information are reconstructed using the dictionary ensemble and view interpolation is performed. In order to realize the pipeline we present several technical contributions including a denoising scheme enhancing the sparsity in the dataset which enables higher compression ratios, and a novel pruning strategy which reduces the size of the dictionary ensemble and leads to significant reductions in computational complexity during the encoding of a light field. Our approach is independent of the light field parameterization and can be used with data from any light field video capture system. To demonstrate the usefulness of our pipeline, we utilize various publicly available light field video datasets and discuss the medical application of documenting heart surgery.Computer graphicsImagebased renderingComputational photographyImage compression10.1111/cgf.13835265-276