{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:58:57Z","timestamp":1767085137630,"version":"3.40.5"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42176196"],"award-info":[{"award-number":["42176196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Taishan Scholars Program","award":["tsqn201909060"],"award-info":[{"award-number":["tsqn201909060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tbdata.2025.3536926","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T19:23:41Z","timestamp":1738265021000},"page":"988-1000","source":"Crossref","is-referenced-by-count":1,"title":["Terrain Scene Generation Using a Lightweight Vector Quantized Generative Adversarial Network"],"prefix":"10.1109","volume":"11","author":[{"given":"Yan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Ocean University of China, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1634-9840","authenticated-orcid":false,"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1047-427X","authenticated-orcid":false,"given":"Xinghui","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Ocean University of China, Qingdao, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3316472"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2018.2863558"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3216566"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"article-title":"Auto-encoding variational bayes","year":"2014","author":"Kingma","key":"ref6"},{"key":"ref7","first-page":"6309","article-title":"Neural discrete representation learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Sys.","author":"Oord","year":"2017"},{"key":"ref8","article-title":"Generating diverse high-fidelity images with VQ-VAE-2","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Sys.","author":"Razavi","year":"2019"},{"key":"ref9","first-page":"4055","article-title":"Image transformer","volume-title":"proc. Int. Conf. Mach. Learn.","author":"Parmar"},{"key":"ref10","first-page":"1747","article-title":"Pixel recurrent neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Van Den Oord"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01268"},{"issue":"2","key":"ref12","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","volume":"32","author":"Rezende","year":"2014"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01043"},{"key":"ref14","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dhariwal"},{"key":"ref15","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.00387"},{"key":"ref18","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. on Mach. Learn.","author":"Nichol"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01103"},{"article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","year":"2017","author":"Howard","key":"ref20"},{"key":"ref21","first-page":"4510","article-title":"Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis. Pattern Recognit.","author":"Howard","year":"2018"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_40"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref25","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"article-title":"SepViT: Separable vision transformer","year":"2022","author":"Li","key":"ref26"},{"article-title":"LightViT: Towards light-weight convolution-free vision transformers","year":"2022","author":"Huang","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"article-title":"Next-ViT: Next generation vision transformer for efficient deployment in realistic industrial scenarios","year":"2022","author":"Li","key":"ref29"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"ref31","first-page":"12 934","article-title":"EfficientFormer: Vision transformers at mobileNet speed","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351084"},{"article-title":"Ultra lightweight image super-resolution with multi-attention layers","year":"2020","author":"Muqeet","key":"ref34"},{"article-title":"ShuffleMixer: An efficient convnet for image super-resolution","year":"2022","author":"Sun","key":"ref35"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01166"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-023-0364-2"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.136"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00215"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9413080"},{"key":"ref57","first-page":"20 343","article-title":"Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475291"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00488"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/128"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00132"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.149"},{"key":"ref64","doi-asserted-by":"crossref","DOI":"10.5244\/C.26.135","article-title":"Low-complexity single-image super-resolution based on nonnegative neighbor embedding","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Bevilacqua","year":"2012"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-4020-z"},{"article-title":"Large scale GAN training for high fidelity natural image synthesis","year":"2018","author":"Brock","key":"ref69"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25082-8_3"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687317\/11003987\/10858453.pdf?arnumber=10858453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T19:37:16Z","timestamp":1747337836000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10858453\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":66,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2025.3536926","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"type":"electronic","value":"2332-7790"},{"type":"electronic","value":"2372-2096"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}