{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:13:49Z","timestamp":1768421629381,"version":"3.49.0"},"reference-count":27,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/icip.2018.8451731","type":"proceedings-article","created":{"date-parts":[[2018,9,7]],"date-time":"2018-09-07T17:59:22Z","timestamp":1536343162000},"page":"445-449","source":"Crossref","is-referenced-by-count":17,"title":["Macro-Pixel Prediction Based on Convolutional Neural Networks for Lossless Compression of Light Field Images"],"prefix":"10.1109","author":[{"given":"Ionut","family":"Schiopu","sequence":"first","affiliation":[]},{"given":"Adrian","family":"Munteanu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"378","article-title":"Improved spatial prediction for 3D holoscopic image and video coding","author":"conti","year":"2011","journal-title":"Proc Eur Signal Process Conf"},{"key":"ref11","first-page":"1","article-title":"High efficiency coding of light field images based on tiling and pseudo-temporal data arrangement","author":"perra","year":"2016","journal-title":"Proc Int Workshop Multimedia & Expo"},{"key":"ref12","first-page":"1","article-title":"Pseudo-sequence-based light field image compression","author":"liu","year":"2016","journal-title":"Proc Int Workshop Multimedia & Expo"},{"key":"ref13","first-page":"131","article-title":"Pseudo sequence based 2-D hierarchical coding structure for light-field image compression","author":"li","year":"2017","journal-title":"Proc Data Compression Conf"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2012.2221191"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/PCS.2016.7906404"},{"key":"ref16","first-page":"1","article-title":"Efficient directional and 11-optimized intra-prediction for light field images","author":"zhong","year":"2017","journal-title":"Proc Int Conf Image Process"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2721104"},{"key":"ref18","first-page":"341","article-title":"Image denoising and inpainting with deep neural networks","author":"xie","year":"0","journal-title":"Proc Int Conf on Neural Information Processing Systems - Volume 1 USA 2012 NIPS&#x2019; 12"},{"key":"ref19","article-title":"Deep convolutional neural fields for depth estimation from a single image","volume":"abs 1411 6387","author":"liu","year":"2014","journal-title":"CoRR"},{"key":"ref4","first-page":"8291","article-title":"Single lens 3D-camera with extended depth-of-field","volume":"8291","author":"perwab","year":"2012","journal-title":"Proc SPIE"},{"key":"ref27","article-title":"JPEG Pleno Database: EPFL light-field data set","year":"0"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPHOT.2009.5559008"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2737967"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178166"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/83.855427"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/3DTV.2017.8280403"},{"key":"ref2","first-page":"1","article-title":"Light field photography with a hand-held plenoptic camera","author":"ng","year":"2005","journal-title":"Stanford University Computer Science Tech Report"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/26.585919"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/34.121783"},{"key":"ref20","article-title":"Learning-based view synthesis for light field cameras","volume":"35","author":"kalantari","year":"2016","journal-title":"ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2010)"},{"key":"ref22","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"Proc Int Conf on Machine Learning (ICML)"},{"key":"ref21","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"abs 1502 3167","author":"ioffe","year":"2015","journal-title":"CoRR"},{"key":"ref24","first-page":"372","article-title":"A method of solving a convex programming problem with convergence rate O(1\/k2)","volume":"27","author":"nesterov","year":"1983","journal-title":"Soviet Mathematics Doklady"},{"key":"ref23","author":"he","year":"2015","journal-title":"Delving deep into rectifiers Surpassing human-level performance on imagenet classification"},{"key":"ref26","article-title":"New Light Field Image Dataset","author":"rerabek","year":"2016","journal-title":"Quality of Experience for Multimedia"},{"key":"ref25","article-title":"ICME 2016 grand challenge: Light-Field Image Compression","year":"0","journal-title":"JBIG and JPEG"}],"event":{"name":"2018 25th IEEE International Conference on Image Processing (ICIP)","location":"Athens","start":{"date-parts":[[2018,10,7]]},"end":{"date-parts":[[2018,10,10]]}},"container-title":["2018 25th IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8436606\/8451009\/08451731.pdf?arnumber=8451731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T09:17:27Z","timestamp":1643188647000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8451731\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/icip.2018.8451731","relation":{},"subject":[],"published":{"date-parts":[[2018,10]]}}}