{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T19:02:39Z","timestamp":1749927759897},"reference-count":13,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2020,5,1]]},"DOI":"10.1587\/transinf.2019edp7264","type":"journal-article","created":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T22:14:00Z","timestamp":1588284840000},"page":"1031-1038","source":"Crossref","is-referenced-by-count":3,"title":["End-to-End Deep ROI Image Compression"],"prefix":"10.1587","volume":"E103.D","author":[{"given":"Hiroaki","family":"AKUTSU","sequence":"first","affiliation":[{"name":"R&D Group, Hitachi, Ltd."}]},{"given":"Takahiro","family":"NARUKO","sequence":"additional","affiliation":[{"name":"R&D Group, Hitachi, Ltd."}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] H. Akutsu and T. Naruko, \u201cEnd-to-end learned roi image compression,\u201d The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019."},{"key":"2","unstructured":"[2] ITU, \u201cHarnessing the internet of things for global development,\u201d 2016."},{"key":"3","unstructured":"[3] IDC, \u201cData age 2025.\u201d https:\/\/www.seagate.com\/files\/www-content\/our-story\/trends\/files\/idc-seagate-dataage-whitepaper.pdf, 2018."},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, \u201cRoad damage detection and classification using deep neural networks with smartphone images,\u201d Computer-Aided Civil and Infrastructure Engineering, vol.33, no.12, pp.1127-1141, 2018. 10.1111\/mice.12387","DOI":"10.1111\/mice.12387"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. Li, W. Zuo, S. Gu, D. Zhao, and D. Zhang, \u201cLearning convolutional networks for content-weighted image compression,\u201d 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.3214-3223, June 2018. 10.1109\/cvpr.2018.00339","DOI":"10.1109\/CVPR.2018.00339"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool, \u201cConditional probability models for deep image compression,\u201d 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp.4394-4402, 2018. 10.1109\/cvpr.2018.00462","DOI":"10.1109\/CVPR.2018.00462"},{"key":"7","unstructured":"[7] D. Minnen, J. Ball\u00e9, and G.D. Toderici, \u201cJoint autoregressive and hierarchical priors for learned image compression,\u201d Thirty-second Conference on Neural Information Processing Systems, NeurIPS 2018, 3-8 Dec. 2018, Montr\u00e9al, Canada, pp.10794-10803, 2018."},{"key":"8","unstructured":"[8] Z. Wang, E.P. Simoncelli, and A.C. Bovik, \u201cMultiscale structural similarity for image quality assessment,\u201d Conference Record of the Asilomar Conference on Signals, Systems and Computers, pp.1398-1402, 2003. 10.1109\/acssc.2003.1292216"},{"key":"9","unstructured":"[9] E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L.V. Gool, \u201cGenerative adversarial networks for extreme learned image compression,\u201d CoRR, vol.abs\/1804.02958, 2018."},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] C. Christopoulos, J. Askelof, and M. Larsson, \u201cEfficient methods for encoding regions of interest in the upcoming jpeg2000 still image coding standard,\u201d IEEE Signal Process. Lett., vol.7, no.9, pp.247-249, Sep. 2000. 10.1109\/97.863146","DOI":"10.1109\/97.863146"},{"key":"11","unstructured":"[11] F. Bellard, \u201cBpg image format.\u201d https:\/\/bellard.org\/bpg\/."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] Z. Wang and Q. Li, \u201cInformation content weighting for perceptual image quality assessment,\u201d IEEE Trans. Image Process., vol.20, no.5, pp.1185-1198, May 2011. 10.1109\/tip.2010.2092435","DOI":"10.1109\/TIP.2010.2092435"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, \u201cScene parsing through ade20k dataset,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5122-5130, 2017. 10.1109\/cvpr.2017.544","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/5\/E103.D_2019EDP7264\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T06:39:06Z","timestamp":1588833546000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/5\/E103.D_2019EDP7264\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,1]]},"references-count":13,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2019edp7264","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,1]]}}}