A geometric space-view redundancy descriptor for light fields: Predicting the compression potential of the JPEG Pleno light field datasets

Abstract

The representation of data in terms of its statistical properties is valuable in many applications. This work uses statistics obtained from 4D scene geometry to characterize, in terms of redundancy, the content produced by lenslet-based light field cameras and by high-density arrays of cameras for the JPEG Pleno Call for Proposals on Light Field Coding. This paper proposes a novel so-called geometric space-view redundancy (GSVR) descriptor, which is able to characterize the amount of redundancy in light fields thus bringing information about the trade-offs involved in effectively exploring redundancy for efficient coding. The redundancy is here measured by the probability, for each block size and range of views, that the image of a given 3D point belongs to the block in all views. For a given probability, the GSVR descriptor models the spaceview correlation, i.e. the correlation between the intra-view block dimensions and the number of views. Therefore, it is a descriptor that has application on dataset selection and encoder control and optimization. The JPEG Pleno datasets are analyzed in terms of the GSVR descriptor in all views.