{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T09:34:03Z","timestamp":1781343243402,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FAPEAM","award":["003\/2018"],"award-info":[{"award-number":["003\/2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total.<\/jats:p>","DOI":"10.3390\/fi14120364","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T03:27:57Z","timestamp":1670383677000},"page":"364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Internet Video Delivery Improved by Super-Resolution with GAN"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1987-6198","authenticated-orcid":false,"given":"Joao da Mata","family":"Liborio","sequence":"first","affiliation":[{"name":"Computing Institute, Federal University of Amazonas (UFAM), Manaus 69080-900, Brazil"},{"name":"Center for Higher Studies of Itacoatiara, Amazonas State University (UEA), Itacoatiara 69101-416, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6868-8548","authenticated-orcid":false,"given":"Cesar","family":"Melo","sequence":"additional","affiliation":[{"name":"Computing Institute, Federal University of Amazonas (UFAM), Manaus 69080-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2037-9831","authenticated-orcid":false,"given":"Marcos","family":"Silva","sequence":"additional","affiliation":[{"name":"Computing Institute, Federal University of Amazonas (UFAM), Manaus 69080-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"ref_1","unstructured":"Cisco VNI (2022, September 05). Cisco Visual Networking Index: Forecast and Trends, 2017\u20132022 White Paper; Technical Report; Cisco. Available online: https:\/\/twiki.cern.ch\/twiki\/pub\/HEPIX\/TechwatchNetwork\/HtwNetworkDocuments\/white-paper-c11-741490.pdf."},{"key":"ref_2","first-page":"34","article-title":"Content Delivery Networks: State of the Art, Trends, and Future Roadmap","volume":"53","author":"Zolfaghari","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TMM.2015.2417771","article-title":"Video Delivery Performance of a Large-Scale VoD System and the Implications on Content Delivery","volume":"17","author":"Li","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_4","unstructured":"BITMOVIN INC (2022, September 05). Per-Title Encoding. Available online: https:\/\/bitmovin.com\/demos\/per-title-encoding."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yan, B., Shi, S., Liu, Y., Yuan, W., He, H., Jana, R., Xu, Y., and Chao, H.J. (2017, January 23\u201327). LiveJack: Integrating CDNs and Edge Clouds for Live Content Broadcasting. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123283"},{"key":"ref_6","unstructured":"Yeo, H., Jung, Y., Kim, J., Shin, J., and Han, D. (2018, January 8\u201310). Neural Adaptive Content-aware Internet Video Delivery. Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), Carlsbad, CA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TNET.2020.2979966","article-title":"DeepCast: Towards Personalized QoE for Edge-Assisted Crowdcast With Deep Reinforcement Learning","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_8","unstructured":"Liborio, J.M., Souza, C.M., and Melo, C.A.V. (2021, January 8\u201310). Super-resolution on Edge Computing for Improved Adaptive HTTP Live Streaming Delivery. Proceedings of the 2021 IEEE tenth International Conference on Cloud Networking (CloudNet), Cookeville, TN, USA."},{"key":"ref_9","unstructured":"Yeo, H., Do, S., and Han, D. (December, January 30). How Will Deep Learning Change Internet Video Delivery?. Proceedings of the 16th ACM Workshop on Hot Topics in Networks, Palo Alto, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1038\/536139a","article-title":"The bandwidth bottleneck that is throttling the Internet","volume":"536","author":"Hecht","year":"2016","journal-title":"Nature"},{"key":"ref_11","unstructured":"Christian, P. (2022, September 05). Int\u2019l Bandwidth and Pricing Trends; Technical Report; TeleGeography: 2018. Available online: https:\/\/www.afpif.org\/wp-content\/uploads\/2018\/08\/01-International-Internet-Bandwidth-and-Pricing-Trends-in-Africa-%E2%80%93-Patrick-Christian-Telegeography.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Z., Sun, L., Wu, C., Zhu, W., and Yang, S. (May, January 27). Joint online transcoding and geo-distributed delivery for dynamic adaptive streaming. Proceedings of the IEEE INFOCOM 2014\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM.2014.6847928"},{"key":"ref_13","unstructured":"AI IMPACTS (2022, August 16). 2019 recent trends in GPU price per FLOPS. Technical report, AI IMPACTS. Available online: https:\/\/aiimpacts.org\/2019-recent-trends-in-gpu-price-per-flops."},{"key":"ref_14","unstructured":"NVIDIA Corporation (2022, August 16). Accelerated Computing Furthermore, The Democratization Of Supercomputing; Technical Report; California, USA, NVIDIA Corporation: 2018. Available online: https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/tesla-product-literature\/sc18-tesla-democratization-tech-overview-r4-web.pdf."},{"key":"ref_15","unstructured":"Cloud, G. (2022, August 16). Cheaper Cloud AI Deployments with NVIDIA T4 GPU Price Cut; Technical Report; California, USA, Google: 2020. Available online: https:\/\/cloud.google.com\/blog\/products\/ai-machine-learning\/cheaper-cloud-ai-deployments-with-nvidia-t4-gpu-price-cut."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Papidas, A.G., and Polyzos, G.C. (2022). Self-Organizing Networks for 5G and Beyond: A View from the Top. Future Internet, 14.","DOI":"10.3390\/fi14030095"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dong, J., and Qian, Q. (2022). A Density-Based Random Forest for Imbalanced Data Classification. Future Internet, 14.","DOI":"10.3390\/fi14030090"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TCI.2016.2532323","article-title":"Video Super-Resolution With Convolutional Neural Networks","volume":"2","author":"Kappeler","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_19","unstructured":"P\u00e9rez-Pellitero, E., Sajjadi, M.S.M., Hirsch, M., and Sch\u00f6lkopf, B. (2018). Photorealistic Video Super Resolution. arXiv."},{"key":"ref_20","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2018). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2018). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_22","unstructured":"Leal-Taix\u00e9, L., and Roth, S. (2019). ESRGAN: Enhanced super-resolution generative adversarial networks. Computer Vision\u2014ECCV 2018 Workshops, Springer International Publishing."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lucas, A., Tapia, S.L., Molina, R., and Katsaggelos, A.K. (2018). Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution. arXiv.","DOI":"10.1109\/ICIP.2018.8451714"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/TMM.2019.2937688","article-title":"MRFN: Multi-Receptive-Field Network for Fast and Accurate Single Image Super-Resolution","volume":"22","author":"He","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, J., Teng, G., and An, P. (2021). Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement. Electronics, 10.","DOI":"10.3390\/electronics10040459"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep Learning for Single Image Super-Resolution: A Brief Review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","first-page":"60","article-title":"A Deep Journey into Super-Resolution: A Survey","volume":"53","author":"Anwar","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Learning a Deep Convolutional Network for Image Super-Resolution. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10578-9"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., and Tang, X. (2016). Accelerating the Super-Resolution Convolutional Neural Network. arXiv.","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2018.01.043","article-title":"CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks","volume":"285","author":"Chen","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. arXiv.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Li, F. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. arXiv.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2019). Recurrent Back-Projection Network for Video Super-Resolution. arXiv.","DOI":"10.1109\/CVPR.2019.00402"},{"key":"ref_35","unstructured":"Tian, Y., Zhang, Y., Fu, Y., and Xu, C. (2018). TDAN: Temporally Deformable Alignment Network for Video Super-Resolution. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K.C.K., Yu, K., Dong, C., and Loy, C.C. (2019). EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. arXiv.","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jo, Y., Oh, S.W., Kang, J., and Kim, S.J. (2018, January 18\u201323). Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00340"},{"key":"ref_38","unstructured":"Isobe, T., Zhu, F., Jia, X., and Wang, S. (2020). Revisiting Temporal Modeling for Video Super-resolution. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s41095-020-0175-7","article-title":"iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks","volume":"6","author":"Chadha","year":"2020","journal-title":"Comput. Vis. Media"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., and Lee, K.M. (2019, January 16\u201317). NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00251"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, C., and Sun, D. (2011, January 20\u201325). A Bayesian approach to adaptive video super resolution. Proceedings of the CVPR 2011, Washington, DC, USA.","DOI":"10.1109\/CVPR.2011.5995614"},{"key":"ref_42","unstructured":"Xue, T., Chen, B., Wu, J., Wei, D., and Freeman, W.T. (2017). Video Enhancement with Task-Oriented Flow. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Liao, R., Wang, J., and Jia, J. (2017, January 22\u201329). Detail-Revealing Deep Video Super-Resolution. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.479"},{"key":"ref_44","unstructured":"Liborio, J.M., and Melo, C.A.V. (2019, January 11\u201313). A GAN to Fight Video-related Traffic Flooding: Super-resolution. Proceedings of the 2019 IEEE Latin-American Conference on Communications (LATINCOM), Salvador, Brazil."},{"key":"ref_45","unstructured":"Lubin, J. (1998, January 8\u201311). A human vision system model for objective image fidelity and target detectability measurements. Proceedings of the Ninth European Signal Processing Conference (EUSIPCO 1998), Rhodes, Greece."},{"key":"ref_46","unstructured":"Watson, A.B. (2021, July 21). Proposal: Measurement of a JND scale for video quality. IEEE G-2.1. 6 Subcommittee on Video Compression Measurements; Citeseer. Available online: https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=2c57d52b6fcdd4e967f9a39e6e7509d948e57a07."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, J.Y.c., Jin, L., Hu, S., Katsavounidis, I., Li, Z., Aaron, A., and Kuo, C.C.J. (2015, January 10\u201313). Experimental design and analysis of JND test on coded image\/video. Proceedings of the Applications of Digital Image Processing XXXVIII, San Diego, CA, USA.","DOI":"10.1117\/12.2188389"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, H., Katsavounidis, I., Zhou, J., Park, J., Lei, S., Zhou, X., Pun, M., Jin, X., Wang, R., and Wang, X. (2017). VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement. arXiv.","DOI":"10.1016\/j.jvcir.2017.04.009"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. arXiv.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_50","unstructured":"Huang, C., Wang, A., Li, J., and Ross, K.W. (2008, January 20\u201322). Measuring and evaluating large-scale CDNs. Proceedings of the ACM IMC, Vouliagmeni, Greece."},{"key":"ref_51","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_53","unstructured":"Maas, A.L. (2013, January 16\u201321). Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the ICML, Atlanta, GA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., and Vanhoucke, V. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_55","unstructured":"Jolicoeur-Martineau, A. (2018). The relativistic discriminator: A key element missing from standard GAN. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss Functions for Image Restoration With Neural Networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_57","unstructured":"Mathieu, M., Couprie, C., and LeCun, Y. (2015). Deep multi-scale video prediction beyond mean square error. arXiv."},{"key":"ref_58","unstructured":"Bruna, J., Sprechmann, P., and LeCun, Y. (2015). Super-Resolution with Deep Convolutional Sufficient Statistics. arXiv."},{"key":"ref_59","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A. (2017). Places: A 10 million Image Database for Scene Recognition. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1167\/17.10.296"},{"key":"ref_61","unstructured":"Zhang, K., Gu, S., and Timofte, R. (2020, January 14\u201319). NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA."},{"key":"ref_62","unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. arXiv."},{"key":"ref_63","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 1","author":"Krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems"},{"key":"ref_64","unstructured":"Li, Z., Bampis, C., Novak, J., Aaron, A., Swanson, K., Moorthy, A., and Cock, J.D. (2021, July 15). VMAF: The Journey Continues. Online, Netflix Technology Blog. Available online: https:\/\/netflixtechblog.com\/vmaf-the-journey-continues-44b51ee9ed12."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Aaron, A., Li, Z., Manohara, M., Lin, J.Y., Wu, E.C.H., and Kuo, C.C.J. (2015, January 27\u201330). Challenges in cloud based ingest and encoding for high quality streaming media. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351097"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/12\/364\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:20Z","timestamp":1760146520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/12\/364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":65,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["fi14120364"],"URL":"https:\/\/doi.org\/10.3390\/fi14120364","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,6]]}}}