Compression of light fields - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2022

Compression of light fields

Milan Stepanov
Frédéric Dufaux

Résumé

Light fields (LFs) enable increasing the degree of realism and immersion of visual experience by capturing a scene with a higher number of dimensions than conventional two-dimensional (2D) imaging. Chapter 6 describes various means of capturing LFs, from a plenoptic camera to an array of cameras. The captured information offers novel applications, such as refocusing and a perspective change, at the cost of increased dimensionality and thus storage demand. The necessity of efficient compression methods was acknowledged by the JPEG committee, which started the JPEG Pleno initiative to provide a standard framework for the representation and coding of plenoptic data. JPEG Pleno organized grand challenges to collect novel LF coding solutions and evaluate them under common conditions following object and subjective quality metrics. These challenges showed that it is possible to exploit correlations in LF images more efficiently using solutions specifically designed to reduce redundancies in LF structure compared to standard image and video codecs. In the following sections, we will overview different coding solutions for the compression of LF contents. We adopt a classification of LF coding solutions based on a functional part of a codec that is responsible for exploiting LF correlation: transformbased and prediction-based solutions, as presented in Fig. 8.1. Three different transforms are considered in the former group of methods and various prediction-based methodologies based on the type of correlation they are trying to exploit. We mention some promising coding solutions leveraging high modeling capabilities of deep learning …
Fichier non déposé

Dates et versions

hal-03801026 , version 1 (06-10-2022)

Identifiants

Citer

Milan Stepanov, Giuseppe Valenzise, Frédéric Dufaux. Compression of light fields. Immersive Video Technologies, Elsevier, 2022, 978-0-323-91755-1. ⟨10.1016/b978-0-32-391755-1.00014-6⟩. ⟨hal-03801026⟩
35 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More