A Novel Compression Scheme Based on Hybrid Tucker-Vector Quantization Via Tensor Sketching for Dynamic Light Fields Acquired Through Coded Aperture Camera

Abstract

Emerging computational light field displays are a suitable choice for realistic presentation of 3D scenes on autostereoscopic glasses-free platforms. However, the enormous size of light field limits their utilization for streaming and 3D display applications. In this paper, we propose a novel representation, coding and streaming scheme for dynamic light fields based on a novel Hybrid Tucker TensorSketch Vector Quantization (HTTSVQ) algorithm. A dynamic light field can be generated from a static light field to capture a moving 3D scene. We acquire images through different coded aperture patterns for a dynamic light field and perform their low-rank approximation using our HTTSVQ scheme, followed by encoding with High Efficiency Video Coding (HEVC). The proposed single pass coding scheme can incrementally handle tensor elements and thus enables to stream and compress light field data without the need to store it in full. Additional encoding of low-rank approximated acquired images by HEVC eliminates intra-frame, inter-frame and intrinsic redundancies in light field data. Comparison with state-of-the-art coders HEVC and its multi-view extension (MV-HEVC) exhibits superior compression performance of the proposed scheme for real-world light fields.