Literature DB >> 27576250

Light Field Multi-View Video Coding With Two-Directional Parallel Inter-View Prediction.

Gengkun Wang, Wei Xiang, Mark Pickering, Chang Wen Chen.   

Abstract

Light field (LF) technology has been popularly adopted by a wide range of conventional industries. However, one problem when dealing with LFs is the sheer size of data volume. There have been many multi-view video coding (MVC)-based LF video coding methods reported in the literature, aiming at finding the best prediction structure for LF video coding. It is clear that the number of possible prediction structures is unlimited, and it is also observed that the coding bit-rate can be reduced by increasing the number of bi-directionally encoded views in the prediction structure. However, none work has been conducted to analyze the relationship of the prediction structure with its coding performance. In light of this observation, we first design a new LF-MVC prediction structure by extending the inter-view prediction into a two-directional parallel structure. Analytical models for source coding rate and encoding time are developed to analyze their relationships with the prediction structure, and are proven to be well-matched to our experimental results. Experimental evaluation of two LF video sequences demonstrates that the proposed LF-MVC prediction structure can achieve a factor of 26% bit-rate reduction against the conventional MVC prediction structure for an LF video with 5×5 views, and a further 34% bit-rate reduction for an LF video with a larger 10×10 views. Compared with the state-of-the-art MVC-based LF video coding prediction structures in the literature, LF-MVC can achieve the best coding performance, and with its high encoding efficiency, is well suited for deployment in practical LF-based 3D systems.

Year:  2016        PMID: 27576250     DOI: 10.1109/TIP.2016.2603602

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  2 in total

1.  A Novel Light Field Image Compression Method Using EPI Restoration Neural Network.

Authors:  Jinghuai Liu; Qian Zhang; Ang Shen; Ying Gao; Jiaqi Hou; Bin Wang; Tao Yan
Journal:  Biomed Res Int       Date:  2022-06-13       Impact factor: 3.246

2.  Attention Networks for the Quality Enhancement of Light Field Images.

Authors:  Ionut Schiopu; Adrian Munteanu
Journal:  Sensors (Basel)       Date:  2021-05-07       Impact factor: 3.576

  2 in total

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