{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:15:13Z","timestamp":1775578513547,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198380","type":"print"},{"value":"9783031198397","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19839-7_6","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T11:40:06Z","timestamp":1666438806000},"page":"88-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bandwidth-Aware Adaptive Codec for\u00a0DNN Inference Offloading in\u00a0IoT"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0215-193X","authenticated-orcid":false,"given":"Xiufeng","family":"Xie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7458-6505","authenticated-orcid":false,"given":"Ning","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7505-9512","authenticated-orcid":false,"given":"Wentao","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Ji","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"6_CR1","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. arXiv preprint arXiv:1308.3432 (2013)"},{"key":"6_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-3-030-58565-5_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Choi","year":"2020","unstructured":"Choi, J., Han, B.: Task-aware quantization network for JPEG image compression. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 309\u2013324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_19"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016). https:\/\/www.cityscapes-dataset.com\/","DOI":"10.1109\/CVPR.2016.350"},{"key":"6_CR4","unstructured":"Courbariaux, M., Bengio, Y., David, J.P.: BinaryConnect: training deep neural networks with binary weights during propagations. In: Advances in Neural Information Processing Systems (2015)"},{"key":"6_CR5","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: training deep neural networks with weights and activations constrained to + 1 or -1. arXiv preprint arXiv:1602.02830 (2016)"},{"issue":"1","key":"6_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/(SICI)1520-6378(199702)22:1<11::AID-COL4>3.0.CO;2-7","volume":"22","author":"H Fairman","year":"1997","unstructured":"Fairman, H., Brill, M., Hemmendinger, H.: How the cie 1931 color-matching functions were derived from wright-guild data. Color. Res. Appl. 22(1), 11\u201323 (1997)","journal-title":"Color. Res. Appl."},{"key":"6_CR7","unstructured":"Google: an image format for the web (2021). https:\/\/developers.google.com\/speed\/webp\/"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR9","doi-asserted-by":"publisher","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 2234\u20132240. International Joint Conferences on Artificial Intelligence Organization (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/309","DOI":"10.24963\/ijcai.2018\/309"},{"key":"6_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/978-3-030-01234-2_48","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y He","year":"2018","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., Han, S.: AMC: AutoML for model compression and acceleration on mobile devices. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 815\u2013832. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_48"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Hiriart-Urruty, J.B., Lemar\u00e9chal, C.: Convex analysis and minimization algorithms I: fundamentals, vol. 305. Springer Science & Business Media (2013). https:\/\/doi.org\/10.1007\/978-3-662-02796-7","DOI":"10.1007\/978-3-662-02796-7"},{"issue":"2","key":"6_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/MCOMSTD.2018.1700050","volume":"2","author":"A Hoglund","year":"2018","unstructured":"Hoglund, A., et al.: Overview of 3GPP release 14 further enhanced MTC. IEEE Commun. Stand. Mag. 2(2), 84\u201389 (2018)","journal-title":"IEEE Commun. Stand. Mag."},{"issue":"6","key":"6_CR14","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MNET.2017.1700082","volume":"31","author":"A Hoglund","year":"2017","unstructured":"Hoglund, A., et al.: Overview of 3GPP release 14 enhanced NB-IoT. IEEE Network 31(6), 16\u201322 (2017)","journal-title":"IEEE Network"},{"issue":"6","key":"6_CR15","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/MCOM.2016.7497765","volume":"54","author":"C Hoymann","year":"2016","unstructured":"Hoymann, C., et al.: LTE release 14 outlook. IEEE Commun. Mag. 54(6), 44\u201349 (2016)","journal-title":"IEEE Commun. Mag."},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Hu, C., Bao, W., Wang, D., Liu, F.: Dynamic adaptive dnn surgery for inference acceleration on the edge. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 1423\u20131431. IEEE (2019)","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"6_CR17","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Advances in Neural Information Processing Systems (2016)"},{"key":"6_CR18","unstructured":"ITUR: BT 601: studio encoding parameters of digital television for standard 4: 3 and wide-screen 16: 9 aspect ratios. ITU-R Rec. BT 656 (1995)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"issue":"1","key":"6_CR20","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1145\/3093337.3037698","volume":"45","author":"Y Kang","year":"2017","unstructured":"Kang, Y., et al.: Neurosurgeon: collaborative intelligence between the cloud and mobile edge. ACM SIGARCH Comput. Archit. News 45(1), 615\u2013629 (2017)","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"6_CR21","unstructured":"Krizhevsky, A., et al.: Learning Multiple Layers of Features from Tiny Images. Tech. rep., University of Toronto (2009). https:\/\/www.cs.toronto.edu\/~kriz\/cifar-10-python.tar.gz"},{"key":"6_CR22","unstructured":"Li, F., Zhang, B., Liu, B.: Ternary Weight Networks. arXiv preprint arXiv:1605.04711 (2016)"},{"key":"6_CR23","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, P.H.: Pruning filters for efficient convNets. In: International Conference on Learning Representations (2017)"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Lin, M., et al.: HRank: filter pruning using high-rank feature map. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1529\u20131538 (2020)","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: DeepN-JPEG: a deep neural network favorable JPEG-based image compression framework. In: Proceedings of the 55th Annual Design Automation Conference, pp. 1\u20136 (2018)","DOI":"10.1145\/3195970.3196022"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Luo, X., Talebi, H., Yang, F., Elad, M., Milanfar, P.: The rate-distortion-accuracy tradeoff: JPEG case study. arXiv preprint arXiv:2008.00605 (2020)","DOI":"10.1109\/DCC50243.2021.00049"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Ma, X., et al.: PCONV: the missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5117\u20135124 (2020)","DOI":"10.1609\/aaai.v34i04.5954"},{"key":"6_CR28","unstructured":"Peng, H., Wu, J., Chen, S., Huang, J.: Collaborative channel pruning for deep networks. In: International Conference on Machine Learning, pp. 5113\u20135122 (2019)"},{"key":"6_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: imagenet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525\u2013542. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Richardson, I.E.: H. 264 and MPEG-4 video compression: video coding for next-generation multimedia. John Wiley & Sons (2004)","DOI":"10.1002\/0470869615"},{"key":"6_CR31","unstructured":"Roelofs, G., Koman, R.: PNG: the definitive guide. O\u2019Reilly & Associates, Inc. (1999)"},{"key":"6_CR32","unstructured":"Shin, R., Song, D.: JPEG-resistant adversarial images. In: NIPS 2017 Workshop on Machine Learning and Computer Security, vol. 1 (2017)"},{"key":"6_CR33","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Wallace, G.K.: The JPEG still picture compression standard. In: IEEE Transactions on Consumer Electronics (1992)","DOI":"10.1109\/30.125072"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Pruning from scratch. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i07.6910"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Xie, X., Kim, K.H.: Source compression with bounded dnn perception loss for IoT edge computer vision. In: The 25th Annual International Conference on Mobile Computing and Networking, pp. 1\u201316 (2019)","DOI":"10.1145\/3300061.3345448"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhu, Y., Liu, J.: ECC: platform-independent energy-constrained deep neural network compression via a bilinear regression model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11206\u201311215 (2019)","DOI":"10.1109\/CVPR.2019.01146"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Quantization networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7308\u20137316 (2019)","DOI":"10.1109\/CVPR.2019.00748"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472\u2013480 (2017)","DOI":"10.1109\/CVPR.2017.75"},{"key":"6_CR40","unstructured":"Zhang, C., Karjauv, A., Benz, P., Kweon, I.S.: Towards robust data hiding against (jpeg) compression: a pseudo-differentiable deep learning approach. arXiv preprint arXiv:2101.00973 (2020)"},{"key":"6_CR41","unstructured":"Zhu, C., Han, S., Mao, H., Dally, W.J.: Trained ternary quantization. arXiv preprint arXiv:1612.01064 (2016)"},{"key":"6_CR42","unstructured":"Zhuang, Z., et al.: Discrimination-aware channel pruning for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 875\u2013886 (2018)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19839-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:18:05Z","timestamp":1709813885000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19839-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198380","9783031198397"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19839-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}