{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:55Z","timestamp":1754156875555,"version":"3.41.2"},"reference-count":43,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2024,8,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-03-2023-0071","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T10:10:20Z","timestamp":1708510220000},"page":"652-668","source":"Crossref","is-referenced-by-count":0,"title":["Light field image coding using a residual channel attention network\u2013based view 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field-of-view scalability and exemplar-based interlayer prediction","volume":"20","year":"2018","journal-title":"IEEE Transactions on Multimedia"},{"year":"2018","first-page":"435","article-title":"A 4D DCT-based lenslet light field codec","key":"key2025010616355356000_ref013"},{"issue":"12","key":"key2025010616355356000_ref014","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","article-title":"Overview of the high efficiency video coding (HEVC) standard","volume":"22","year":"2012","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"year":"2017","first-page":"19","article-title":"A dataset and evaluation methodology for depth estimation on 4D light fields","key":"key2025010616355356000_ref015"},{"year":"2021","first-page":"690","article-title":"Multiple description coding for best-effort delivery of light field video using GNN-based compression","key":"key2025010616355356000_ref016"},{"year":"2020","first-page":"128","article-title":"Random-access-aware light field video coding using tree pruning method","key":"key2025010616355356000_ref017"},{"issue":"1","key":"key2025010616355356000_ref018","first-page":"177","article-title":"Light field image compression using generative adversarial network-based view synthesis","volume":"9","year":"2018","journal-title":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems"},{"issue":"8","key":"key2025010616355356000_ref019","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/TIP.2018.2832449","article-title":"Plenoptic image coding using macropixel-based intra prediction","volume":"27","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"year":"2019","first-page":"588","article-title":"A new prediction structure for efficient MV-HEVC based light field video compression","key":"key2025010616355356000_ref020"},{"year":"2014","article-title":"Adam: a method for stochastic optimization","key":"key2025010616355356000_ref021"},{"issue":"11","key":"key2025010616355356000_ref022","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1364\/JOSAA.32.002021","article-title":"Computational photography with plenoptic camera and light field capture: tutorial","volume":"32","year":"2015","journal-title":"JOSA A"},{"year":"1996","first-page":"31","article-title":"Light field rendering","key":"key2025010616355356000_ref023"},{"key":"key2025010616355356000_ref024","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.ins.2020.07.073","article-title":"View synthesis-based light field image compression using a generative adversarial network","volume":"545","year":"2021","journal-title":"Information Sciences"},{"key":"key2025010616355356000_ref025","first-page":"4400","article-title":"Multi-stream dense view reconstruction network for light field image compression","volume":"25","year":"2022","journal-title":"IEEE Transactions on Multimedia"},{"year":"2019","first-page":"3567","article-title":"An efficient random access light field video compression utilizing diagonal inter-view prediction","key":"key2025010616355356000_ref026"},{"unstructured":"Ng, R., Levoy, M., Br\u00e9dif, M., Duval, G., Horowitz, M. and Hanrahan, P. (2005), \u201cLight field photography with a hand-held plenoptic camera\u201d, Stanford University Computer Science Tech Report, Stanford University, Stanford, CA, USA.","key":"key2025010616355356000_ref027"},{"unstructured":"Raj, S., Michael, L. and Sunder, A. (2016), \u201cStanford lytro light field archive[EB\/OL]\u201d, available at: http:\/\/lightfields.stanford.edu\/ (accessed October 2016).","key":"key2025010616355356000_ref028"},{"year":"2016","article-title":"New light field image dataset","key":"key2025010616355356000_ref029"},{"year":"2018","first-page":"445","article-title":"Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images","key":"key2025010616355356000_ref030"},{"key":"key2025010616355356000_ref031","article-title":"Deep-learning-based macro-pixel synthesis and lossless coding of light field images","volume":"8","year":"2019","journal-title":"APSIPA Transactions on Signal and Information Processing"},{"year":"2014","article-title":"Very deep convolutional networks for large-scale image recognition","key":"key2025010616355356000_ref032"},{"year":"2021","first-page":"1","article-title":"A study on 4d light field compression using multi-focus images and reference views","key":"key2025010616355356000_ref033"},{"year":"2017","article-title":"Sfm-net: learning of structure and motion from video","key":"key2025010616355356000_ref034"},{"issue":"6","key":"key2025010616355356000_ref035","doi-asserted-by":"crossref","first-page":"7527","DOI":"10.1007\/s11042-022-11955-8","article-title":"Learning-based high-efficiency compression framework for light field videos","volume":"81","year":"2022","journal-title":"Multimedia Tools and Applications"},{"issue":"11","key":"key2025010616355356000_ref036","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1109\/JDT.2014.2361147","article-title":"Augmented reality 3D displays with micro integral imaging","volume":"11","year":"2015","journal-title":"Journal of Display Technology"},{"issue":"4","key":"key2025010616355356000_ref037","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","year":"2004","journal-title":"IEEE Transactions on Image Processing"},{"issue":"7","key":"key2025010616355356000_ref038","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/JSTSP.2017.2747126","article-title":"Light field image processing: an overview","volume":"11","year":"2017","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"year":"2018","first-page":"286","article-title":"Image super-resolution using very deep residual channel attention networks","key":"key2025010616355356000_ref039"},{"year":"2019","first-page":"8564","article-title":"Light field image compression using depth-based CNN in intra prediction","key":"key2025010616355356000_ref040"},{"year":"2020","first-page":"1","article-title":"Light field image coding using dual discriminator generative adversarial network and VVC temporal scalability","key":"key2025010616355356000_ref041"},{"year":"2001","article-title":"Calculation of average PSNR differences between RD-curves","key":"key2025010616355356000_ref042"},{"year":"2019","article-title":"JPEG Pleno light field coding common test conditions v3. 2","key":"key2025010616355356000_ref043"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-03-2023-0071\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-03-2023-0071\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:15:03Z","timestamp":1753398903000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/58\/4\/652-668\/1216719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,21]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2,21]]},"published-print":{"date-parts":[[2024,8,5]]}},"alternative-id":["10.1108\/DTA-03-2023-0071"],"URL":"https:\/\/doi.org\/10.1108\/dta-03-2023-0071","relation":{},"ISSN":["2514-9288","2514-9288"],"issn-type":[{"type":"print","value":"2514-9288"},{"type":"electronic","value":"2514-9288"}],"subject":[],"published":{"date-parts":[[2024,2,21]]}}}