Low complexity high efficiency coding of light fields using ensemble classifiers

• Published in: Journal of visual communication and image representation 2020-01, Vol.66, p.102742, Article 102742
• Main Authors: Tahir, Muhammad, Taj, Imtiaz A., Assuncao, Pedro A., Asif, Muhammad
• Format: Article
• Language: English
• Subjects: Fast encoding; HEVC; Light fields; Machine learning; Random forests
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2019.102742

•Detailed statistical analysis to highlight unique characteristics of light fields coded data.•Fast HEVC encoding of light fields in different representation formats using Random Forests.•Score fusion approach for selection of optimal features for training of classifier models.•Early prediction of SKIP mode and CU depth levels in HEVC encoding of light fields. Light field images can be efficiently compressed using standard video codecs, such as the High Efficiency Video Coding (HEVC). However, the huge amount of data combined with the high computational complexity of HEVC, poses limitations on high-speed light field capturing and storage. This paper presents a contribution for low complexity encoding of light fields, in different formats using HEVC, based on a Random Forests ensemble classifier. Optimal features for training the classifier are found through a score fusion based approach. Using the HEVC still image profile, the proposed method gives speed-up of 56.23% for sub-aperture images. For pseudo video format, the proposed method outperforms others available in the literature, yielding an average speed-up of 62.18%, 56.54% and 44.73% for Random Access, Low-delay Main and All-Intra profiles respectively, with negligible decrease in RD performance. These are novel results in fast coding of light fields, which are useful for further research and benchmarking.