{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:28:52Z","timestamp":1768296532429,"version":"3.49.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T00:00:00Z","timestamp":1710720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Zhejiang Provincial Key R&D","award":["2024C03050"],"award-info":[{"award-number":["2024C03050"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>With the development of embedded systems and deep learning, it is feasible to combine them for offering various and convenient human-centered services, which is based on high-quality (HQ) videos. However, due to the limit of video traffic load and unavoidable noise, the visual quality of an image from an edge camera may degrade significantly, influencing the overall video and service quality. To maintain video stability, video quality enhancement (QE), aiming at recovering HQ videos from their distorted low-quality (LQ) sources, has aroused increasing attention in recent years. The key challenge for video QE lies in how to effectively aggregate complementary information from multiple frames (i.e., temporal fusion). To handle diverse motion in videos, existing methods commonly apply motion compensation before the temporal fusion. However, the motion field estimated from the distorted LQ video tends to be inaccurate and unreliable, thereby resulting in ineffective fusion and restoration. In addition, motion estimation for consecutive frames is generally conducted in a pairwise manner, which leads to expensive and inefficient computation. In this article, we propose a fast yet effective temporal fusion scheme for video QE by incorporating a novel Spatio-Temporal Deformable Convolution (STDC) to simultaneously compensate motion and aggregate temporal information. Specifically, the proposed temporal fusion scheme takes a target frame along with its adjacent reference frames as input to jointly estimate an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from multiple frames can be fused within the STDC operation in one forward pass. Extensive experimental results on three benchmark datasets show that our method performs favorably to the state of the art in terms of accuracy and efficiency.<\/jats:p>","DOI":"10.1145\/3645113","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T12:05:12Z","timestamp":1707393912000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["STDF: Spatio-Temporal Deformable Fusion for Video Quality Enhancement on Embedded Platforms"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7307-6629","authenticated-orcid":false,"given":"Jianing","family":"Deng","sequence":"first","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5601-5912","authenticated-orcid":false,"given":"Shunjie","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2480-6191","authenticated-orcid":false,"given":"Lvcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-4034","authenticated-orcid":false,"given":"Jingtong","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2610-7522","authenticated-orcid":false,"given":"Cheng","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2729891"},{"key":"e_1_3_2_3_2","first-page":"956","volume-title":"Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium","author":"Bi Fukun","year":"2020","unstructured":"Fukun Bi, Jiayi Sun, Mingyang Lei, Yanping Wang, and Xiaodi Sun. 2020. Remote sensing target tracking for UAV aerial videos based on multi-frequency feature enhancement. In Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS\u201920). IEEE, 956\u2013959."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2009.932162"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.38"},{"key":"e_1_3_2_6_2","first-page":"4778","volume-title":"Proceedings of the 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Caballero Jose","year":"2017","unstructured":"Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. 2017. Real-time video super-resolution with spatio-temporal networks and motion compensation. In Proceedings of the 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 4778\u20134787."},{"issue":"3","key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TSP.2013.2290508","article-title":"Reducing artifacts in JPEG decompression via a learned dictionary","volume":"62","author":"Chang Huibin","year":"2013","unstructured":"Huibin Chang, Michael K. Ng, and Tieyong Zeng. 2013. Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Transactions on Signal Processing 62, 3 (2013), 718\u2013728.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"e_1_3_2_8_2","article-title":"MMDetection: Open MMLab detection toolbox and benchmark","author":"Chen Kai","year":"2019","unstructured":"Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. 2019. MMDetection: Open MMLab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019).","journal-title":"arXiv preprint arXiv:1906.07155"},{"key":"e_1_3_2_9_2","first-page":"764","volume-title":"Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917)","author":"Dai Jifeng","year":"2017","unstructured":"Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. 2017. Deformable convolutional networks. In Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917). 764\u2013773."},{"key":"e_1_3_2_10_2","first-page":"10696","volume-title":"Proceedings of the 2020 AAAI Conference on Artificial Intelligence","volume":"34","author":"Deng Jianing","year":"2020","unstructured":"Jianing Deng, Li Wang, Shiliang Pu, and Cheng Zhuo. 2020. Spatio-temporal deformable convolution for compressed video quality enhancement. In Proceedings of the 2020 AAAI Conference on Artificial Intelligence, Vol. 34. 10696\u201310703."},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2015.73","article-title":"Compression artifacts reduction by a deep convolutional network","author":"Dong Chao","year":"2015","unstructured":"Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang. 2015. Compression artifacts reduction by a deep convolutional network. In Proceedings of the 2015 International Conference on Computer Vision (ICCV\u201915). 576\u2013584.","journal-title":"Proceedings of the 2015 International Conference on Computer Vision (ICCV\u201915)"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.891788"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.517","article-title":"Deep generative adversarial compression artifact removal","author":"Galteri Leonardo","year":"2017","unstructured":"Leonardo Galteri, Lorenzo Seidenari, Marco Bertini, and Alberto Del Bimbo. 2017. Deep generative adversarial compression artifact removal. In Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917). 4836\u20134845.","journal-title":"Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917)"},{"key":"e_1_3_2_14_2","first-page":"1193","article-title":"A super-resolution enhancement of UAV images based on a convolutional neural network for mobile devices","author":"Gonz\u00e1lez Daniel","year":"2019","unstructured":"Daniel Gonz\u00e1lez, Miguel A. Patricio, Antonio Berlanga, and Jos\u00e9 M. Molina. 2019. A super-resolution enhancement of UAV images based on a convolutional neural network for mobile devices. Personal and Ubiquitous Computing 26 (2019), 1193\u20131204.","journal-title":"Personal and Ubiquitous Computing"},{"key":"e_1_3_2_15_2","article-title":"MFQE 2.0: A new approach for multi-frame quality enhancement on compressed video","author":"Guan Zhenyu","year":"2019","unstructured":"Zhenyu Guan, Qunliang Xing, Mai Xu, Ren Yang, Tie Liu, and Zulin Wang. 2019. MFQE 2.0: A new approach for multi-frame quality enhancement on compressed video. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published Online, October 2, 2019.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"e_1_3_2_16_2","article-title":"Deep residual learning for image recognition","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 770\u2013778.","journal-title":"Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201916)"},{"key":"e_1_3_2_17_2","volume-title":"Computer Vision\u2014ECCV 2012","author":"Jancsary Jeremy","year":"2012","unstructured":"Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother. 2012. Loss-specific training of non-parametric image restoration models: A new state of the art. In Computer Vision\u2014ECCV 2012. Lecture Notes in Computer Science, Vol. 7578. Springer, 112\u2013125."},{"key":"e_1_3_2_18_2","first-page":"3224","volume-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition","author":"Jo Younghyun","year":"2018","unstructured":"Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. 2018. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. 3224\u20133232."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2532323"},{"key":"e_1_3_2_20_2","first-page":"1725","volume-title":"Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201914)","author":"Karpathy Andrej","year":"2014","unstructured":"Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. 2014. Large-scale video classification with convolutional neural networks. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201914). 1725\u20131732."},{"key":"e_1_3_2_21_2","first-page":"1646","volume-title":"Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201916)","author":"Kim Ji Won","year":"2016","unstructured":"Ji Won Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 1646\u20131654."},{"key":"e_1_3_2_22_2","first-page":"111","volume-title":"Proceedings of the 2018 European Conference on Computer Vision (ECCV\u201918)","author":"Kim TaeHyun","year":"2018","unstructured":"TaeHyun Kim, Mehdi S. M. Sajjadi, Michael Hirsch, and Scholkopf Bernhard. 2018. Spatio-temporal transformer network for video restoration. In Proceedings of the 2018 European Conference on Computer Vision (ECCV\u201918). 111\u2013127."},{"key":"e_1_3_2_23_2","article-title":"Adam: A method for stochastic optimization","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014).","journal-title":"arXiv:1412.6980"},{"key":"e_1_3_2_24_2","article-title":"Deep Laplacian pyramid networks for fast and accurate super-resolution","author":"Lai Wei-Sheng","year":"2017","unstructured":"Wei-Sheng Lai, Jia-Bin Huang, N. Ahuja, and Ming-Hsuan Yang. 2017. Deep Laplacian pyramid networks for fast and accurate super-resolution. In Proceedings of the 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 5835\u20135843.","journal-title":"Proceedings of the 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201917)"},{"key":"e_1_3_2_25_2","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","author":"Li Zewen","year":"2021","unstructured":"Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou. 2021. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems. Published Online, June 10, 2021.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems."},{"issue":"2","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TPAMI.2013.127","article-title":"On Bayesian adaptive video super resolution","volume":"36","author":"Liu Ce","year":"2013","unstructured":"Ce Liu and Deqing Sun. 2013. On Bayesian adaptive video super resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 2 (2013), 346\u2013360.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_27_2","first-page":"568","volume-title":"Proceedings of the 2018 European Conference on Computer Vision (ECCV\u201918)","author":"Lu Guo","year":"2018","unstructured":"Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Zhiyong Gao, and Ming-Ting Sun. 2018. Deep Kalman filtering network for video compression artifact reduction. In Proceedings of the 2018 European Conference on Computer Vision (ECCV\u201918). 568\u2013584."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2943214"},{"key":"e_1_3_2_29_2","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"Maas Andrew L.","year":"2013","unstructured":"Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the 2013 30th International Conference on Machine Learning (ICML\u201913). 1\u20136.","journal-title":"Proceedings of the 2013 30th International Conference on Machine Learning (ICML\u201913)"},{"key":"e_1_3_2_30_2","first-page":"1","volume-title":"Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS\u201919)","author":"Magoulianitis Vasileios","year":"2019","unstructured":"Vasileios Magoulianitis, Dimitrios Ataloglou, Anastasios Dimou, Dimitrios Zarpalas, and Petros Daras. 2019. Does deep super-resolution enhance UAV detection? In Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS\u201919). IEEE, 1\u20136."},{"issue":"3","key":"e_1_3_2_31_2","first-page":"417","article-title":"Towards real-time object detection on embedded systems","volume":"6","author":"Mao Huizi","year":"2016","unstructured":"Huizi Mao, Song Yao, Tianqi Tang, Boxun Li, Jun Yao, and Yu Wang. 2016. Towards real-time object detection on embedded systems. IEEE Transactions on Emerging Topics in Computing 6, 3 (2016), 417\u2013431.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"key":"e_1_3_2_32_2","article-title":"A robust quality enhancement method based on joint spatial-temporal priors for video coding","author":"Meng Xiandong","year":"2020","unstructured":"Xiandong Meng, Xuan Deng, Shuyuan Zhu, Xinfeng Zhang, and Bing Zeng. 2020. A robust quality enhancement method based on joint spatial-temporal priors for video coding. IEEE Transactions on Circuits and Systems for Video Technology. Published Online, August 27, 2020.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology."},{"key":"e_1_3_2_33_2","unstructured":"Christopher Montgomery. 2021. Xiph.org Video Test Media (Derf\u2019s Collection) the Xiph Open Source Community 1994. Retrieved February 16 2024 from https:\/\/medialxiph.org\/video\/derf"},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.37","article-title":"Video frame interpolation via adaptive separable convolution","author":"Niklaus Simon","year":"2017","unstructured":"Simon Niklaus, Long Mai, and Feng Liu. 2017. Video frame interpolation via adaptive separable convolution. In Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917). 261\u2013270.","journal-title":"Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917)"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2012.2221192"},{"key":"e_1_3_2_36_2","first-page":"6626","volume-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition","author":"Sajjadi Mehdi S. M.","year":"2018","unstructured":"Mehdi S. M. Sajjadi, Raviteja Vemulapalli, and Matthew Brown. 2018. Frame-recurrent video super-resolution. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. 6626\u20136634."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2042111"},{"key":"e_1_3_2_38_2","first-page":"806","volume-title":"Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201914)","author":"Razavian Ali Sharif","year":"2014","unstructured":"Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. 2014. CNN features off-the-shelf: An astounding baseline for recognition. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201914). 806\u2013813."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"e_1_3_2_40_2","article-title":"MemNet: A persistent memory network for image restoration","author":"Tai Ying","year":"2017","unstructured":"Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu. 2017. MemNet: A persistent memory network for image restoration. In Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917). 4549\u20134557.","journal-title":"Proceedings of the 2017 International Conference on Computer Vision (ICCV\u201917)"},{"key":"e_1_3_2_41_2","article-title":"Detail-revealing deep video super-resolution","author":"Tao Xin","year":"2017","unstructured":"Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, and Jiaya Jia. 2017. Detail-revealing deep video super-resolution. In Proceedings of the 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 4472\u20134480.","journal-title":"Proceedings of the 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201917)."},{"key":"e_1_3_2_42_2","first-page":"3360","volume-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Tian Yapeng","year":"2020","unstructured":"Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. 2020. TDAN: Temporally-deformable alignment network for video super-resolution. In Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3360\u20133369."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2967596"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"issue":"2","key":"e_1_3_2_46_2","first-page":"392","article-title":"DAC-SDC low power object detection challenge for UAV applications","volume":"43","author":"Xu Xiaowei","year":"2019","unstructured":"Xiaowei Xu, Xinyi Zhang, Bei Yu, Xiaobo Sharon Hu, Christopher Rowen, Jingtong Hu, and Yiyu Shi. 2019. DAC-SDC low power object detection challenge for UAV applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 2 (2019), 392\u2013403.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_47_2","first-page":"7043","volume-title":"Proceedings of the 2019 IEEE International Conference on Computer Vision","author":"Xu Yi","year":"2019","unstructured":"Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, and Huyang Sun. 2019. Non-local ConvLSTM for video compression artifact reduction. In Proceedings of the 2019 IEEE International Conference on Computer Vision. 7043\u20137052."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-01144-2"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/ICME.2019.00098","volume-title":"Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME\u201919)","author":"Yang Ren","year":"2019","unstructured":"Ren Yang, Xiaoyan Sun, Mai Xu, and Wenjun Zeng. 2019. Quality-gated convolutional LSTM for enhancing compressed video. In Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME\u201919). 532\u2013537."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Ren Yang Mai Xu Zulin Wang and Tianyi Li. 2018. Multi-frame quality enhancement for compressed video. In Proceedings of the 2018 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201918). 6664\u20136673.","DOI":"10.1109\/CVPR.2018.00697"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2274386"},{"key":"e_1_3_2_53_2","first-page":"286","volume-title":"Proceedings of the 2018 European Conference on Computer Vision (ECCV\u201918)","author":"Zhang Yulun","year":"2018","unstructured":"Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. 2018. Image super-resolution using very deep residual channel attention networks. In Proceedings of the 2018 European Conference on Computer Vision (ECCV\u201918). 286\u2013301."},{"key":"e_1_3_2_54_2","volume-title":"Proceedings of the 2019 International Conference on Learning Representations (ICLR\u201919)","author":"Zhang Yulun","year":"2019","unstructured":"Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu. 2019. Residual non-local attention networks for image restoration. In Proceedings of the 2019 International Conference on Learning Representations (ICLR\u201919)."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2018.2879368"},{"key":"e_1_3_2_56_2","article-title":"Deformable ConvNets v2: More deformable, better results","author":"Zhu Xizhou","year":"2019","unstructured":"Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. 2019. Deformable ConvNets v2: More deformable, better results. In Proceedings of the 2019 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201919). 9308\u20139316.","journal-title":"Proceedings of the 2019 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201919)"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3645113","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3645113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:28Z","timestamp":1750291408000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3645113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,18]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3,31]]}},"alternative-id":["10.1145\/3645113"],"URL":"https:\/\/doi.org\/10.1145\/3645113","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"value":"1539-9087","type":"print"},{"value":"1558-3465","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,18]]},"assertion":[{"value":"2023-02-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-13","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}