Dense Light Field Coding: A Survey

被引：60

作者：

Conti, Caroline ^{[1
]}

Soares, Luis Ducla ^{[1
]}

Nunes, Paulo ^{[1
]}

机构：

[1] Inst Univ Lisboa ISCTE IUL, Inst Telecomunicacoes, P-1649026 Lisbon, Portugal

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Image coding; Encoding; Transform coding; Standardization; Focusing; Camera array; image compression; light field; plenoptic; video compression; KARHUNEN-LOEVE TRANSFORM; QUALITY ASSESSMENT; IMAGE COMPRESSION; DEPTH ESTIMATION; INTEGRAL IMAGES; EFFICIENT COMPRESSION; ENHANCED COMPRESSION; RESIDUAL IMAGES; SUB-IMAGES; MOTION;

D O I：

10.1109/ACCESS.2020.2977767

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.

引用

页码：49244 / 49284

页数：41

共 245 条

[1] SLIC Superpixels Compared to State-of-the-Art Superpixel Methods [J].

Achanta, Radhakrishna ;

Shaji, Appu ;

Smith, Kevin ;

Lucchi, Aurelien ;

Fua, Pascal ;

Suesstrunk, Sabine .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2012, 34 (11) :2274-2281

[2] Motion and disparity estimation with self adapted evolutionary strategy in 3D video coding [J].

Adedoyin, S. ;

Femando, W. A. C. ;

Aggoun, A. ;

Kondoz, K. M. .

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2007, 53 (04) :1768-1775

[3] A joint motion & disparity motion estimation technique for 3D integral video compression using evolutionary strategy [J].

Adedoyin, S. ;

Fernando, W. A. C. ;

Aggoun, A. .

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2007, 53 (02) :732-739

[4]

Adedoyin S., 2007, PROC IEEE INT C IMAG, V3, pIII

[5] Towards a quality metric for dense light fields [J].

Adhikarla, Vamsi Kiran ;

Vinkler, Marek ;

Sumin, Denis ;

Mantiuk, Rafal K. ;

Myszkowski, Karol ;

Seidel, Hans-Peter ;

Didyk, Piotr .

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :3720-3729

[6] Compression of 3D Integral Images Using 3D Wavelet Transform [J].

Aggoun, Amar .

JOURNAL OF DISPLAY TECHNOLOGY, 2011, 7 (11) :586-592

[7]

Ahmad W, 2017, IEEE IMAGE PROC, P4557, DOI 10.1109/ICIP.2017.8297145

[8]

Ahmad W, 2018, IEEE IMAGE PROC, P654, DOI 10.1109/ICIP.2018.8451051

[9] Shearlet Transform Based Prediction Scheme for Light Field Compression [J].

Ahmad, Waqas ;

Vagharshakyan, Suren ;

Sjostrom, Marten ;

Gotchev, Atanas ;

Bregovic, Robert ;

Olsson, Roger .

2018 DATA COMPRESSION CONFERENCE (DCC 2018), 2018, :396-396

[10]

Alpaslan Z., 2019, ISOIEC JTC1SC29WG1

← 1 2 3 4 5 6 7 8 9 10 →