A Novel Randomize Hierarchical Extension of MV-HEVC for Improved light Field Compression
Mansi Sharma, Gowtham Ragavan
In Proceedings of 2019 International Conference on 3D Immersion (IC3D 2019), Brussels, Belgium,
Part of Stereopsia (World Immersion Forum)
Abstract
This paper presents a novel scheme for light field compression based on a randomize hierarchical multi-view extension of high efficiency video coding (dubbed as RH-MVHEVC). Specifically, a light field data are arranged as a multiple pseudo temporal video sequences which are efficiently compressed with MV-HEVC encoder, following an integrated random coding technique and hierarchical prediction scheme. The critical advantage of proposed RH-MVHEVC scheme is that it utilizes not just a temporal and inter-view prediction, but efficiently exploits the strong intrinsic similarities within each sub-aperture image and among neighboring sub-aperture images in both horizontal and vertical directions. Experimental results consistently outperform the state-of-the-art compression methods on benchmark ICME 2016 and ICIP 2017 grand challenge data sets. It achieves an average up to 33.803% BD-rate reduction and 1.7978 dB BD-PSNR improvement compared with an advanced JEM video encoder, and an average 20.4156% BD-rate reduction and 2.0644 dB BD-PSNR improvement compared with a latest image-based JEM-anchor coding scheme.
HierarchicalMVHEVCLFCodingCRpaper.pdfCitation
M. Sharma and G. Ragavan, "A Novel Randomize Hierarchical Extension of MV-HEVC for Improved Light Field Compression," 2019 International Conference on 3D Immersion (IC3D), Brussels, Belgium, 2019, pp. 1-8. doi: 10.1109/IC3D48390.2019.8975905
BibTex
@INPROCEEDINGS{8975905,
author={M. {Sharma} and G. {Ragavan}},
booktitle={2019 International Conference on 3D Immersion (IC3D)},
title={A Novel Randomize Hierarchical Extension of MV-HEVC for Improved Light Field Compression},
year={2019},
volume={},
number={},
pages={1-8},
keywords={Light field;MV-HEVC;plenoptic image compression;multi-view pseudo sequence;rate allocation;randomized coding;hierarchical prediction structure},
doi={10.1109/IC3D48390.2019.8975905},
ISSN={2379-1772},
month={Dec}}
