Graph-Based Spatio-Angular Prediction for Quasi-Lossless Compression of Light Fields

Graph-based transforms have been shown to be powerful tools for image compression. However, the computation of the basis functions becomes rapidly untractable when the support increases, i.e. when the dimension of the data is high as in the case of light fields. Local transforms with limited supports have been investigated to cope with this difficulty. Nevertheless, the locality of the support may not allow us to fully exploit long term dependencies in the signal. In this paper, we describe a graph based prediction solution that allows taking advantage of intra prediction mechanisms as well as of the good energy compaction properties of the graph transform. The approach relies on a separable spatio-angular transform and derives low frequency spatio-angular coefficients from one single compressed reference view and from the high angular frequency coefficients. In the tests, we used HEVC-Intra, with QP=0, to encode the reference frame with high quality. The high angular frequency coefficients containing very little energy are coded using a simple entropy coder. The approach is shown to be very efficient in a context of high quality quasi-lossless compression of light fields.