High dimensional optical data — varifocal multiview imaging, compression and evaluation

Abstract

Varifocal multiview (VFMV) is an emerging high-dimensional optical data in computational imaging and displays. It describes scenes in angular, spatial, and focal dimensions, whose complex imaging conditions involve dense viewpoints, high spatial resolutions, and variable focal planes, resulting in difficulties in data compression. In this paper, we propose an efficient VFMV compression scheme based on view mountain-shape rearrangement (VMSR) and all-directional prediction structure (ADPS). The VMSR rearranges the irregular VFMV to form a new regular VFMV with mountain-shape focusing distributions. This special rearrangement features prominently in enhancing inter-view correlations by smoothing focusing status changes and moderating view displacements. Then, the ADPS efficiently compresses the rearranged VFMV by exploiting the enhanced correlations. It conducts row-wise hierarchy divisions and creates prediction dependencies among views. The closest adjacent views from all directions serve as reference frames to improve the prediction efficiency. Extensive experiments demonstrate the proposed scheme outperforms comparison schemes by quantitative, qualitative, complexity, and forgery protection evaluations. As high as 3.17 dB gains of peak signal-to-noise ratio (PSNR) and 61.1% bitrate savings can be obtained, achieving the state-of-the-art compression performance. VFMV is also validated could serve as a novel secure imaging format protecting optical data against the forgery of large models.