{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:49:34Z","timestamp":1781884174971,"version":"3.54.5"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585358","type":"print"},{"value":"9783030585365","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58536-5_27","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T23:02:42Z","timestamp":1604358162000},"page":"456-472","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Content Adaptive and Error Propagation Aware Deep Video Compression"],"prefix":"10.1007","author":[{"given":"Guo","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunlei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanli","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"27_CR1","unstructured":"Bellard, F.: BPG image format. http:\/\/bellard.org\/bpg\/. Accessed 30 Oct 2018"},{"key":"27_CR2","unstructured":"Ultra video group test sequences. http:\/\/ultravideo.cs.tut.fi. Accessed 30 Oct 2018"},{"key":"27_CR3","unstructured":"Video trace library (VTL) dataset. http:\/\/trace.kom.aau.dk\/. Accessed 30 Oct 2018"},{"key":"27_CR4","unstructured":"Webp. https:\/\/developers.google.com\/speed\/webp\/. Accessed 30 Oct 2018"},{"key":"27_CR5","unstructured":"Agustsson, E., et al.: Soft-to-hard vector quantization for end-to-end learning compressible representations. In: NIPS, pp. 1141\u20131151 (2017)"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Gool, L.V.: Generative adversarial networks for extreme learned image compression. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, pp. 221\u2013231. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00031"},{"issue":"1","key":"27_CR7","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"1974","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (1974)","journal-title":"IEEE Trans. Comput."},{"key":"27_CR8","unstructured":"Ball\u00e9, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: Proceedings of the 5th International Conference on Learning Representations, ICLR (2017)"},{"key":"27_CR9","unstructured":"Ball\u00e9, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. In: Proceedings of the 6th International Conference on Learning Representations, ICLR (2018)"},{"key":"27_CR10","unstructured":"Bjontegaard, G.: Calculation of average PSNR differences between RD-curves. VCEG-M33 (2001)"},{"key":"27_CR11","unstructured":"Campos, J., Meierhans, S., Djelouah, A., Schroers, C.: Content adaptive optimization for neural image compression. In: IEEE CVPR Workshops 2019 (2019)"},{"issue":"2","key":"27_CR12","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/TCSVT.2019.2892608","volume":"30","author":"Z Chen","year":"2020","unstructured":"Chen, Z., He, T., Jin, X., Wu, F.: Learning for video compression. IEEE Trans. Circuits Syst. Video Techn. 30(2), 566\u2013576 (2020). https:\/\/doi.org\/10.1109\/TCSVT.2019.2892608","journal-title":"IEEE Trans. Circuits Syst. Video Techn."},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Learning image and video compression through spatial-temporal energy compaction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 10071\u201310080 (2019)","DOI":"10.1109\/CVPR.2019.01031"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Choi, Y., El-Khamy, M., Lee, J.: Variable rate deep image compression with a conditional autoencoder. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, pp. 3146\u20133154. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00324"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Djelouah, A., Campos, J., Schaub-Meyer, S., Schroers, C.: Neural inter-frame compression for video coding. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00652"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Habibian, A., van Rozendaal, T., Tomczak, J.M., Cohen, T.: Video compression with rate-distortion autoencoders. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, pp. 7032\u20137041. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00713"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Hu, Z., Chen, Z., Xu, D., Lu, G., Ouyang, W., Gu, S.: Improving deep video compression by resolution-adaptive flow coding. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58536-5_12"},{"key":"27_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: Learning convolutional networks for content-weighted image compression. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00339"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Lu, G., Ouyang, W., Xu, D., Zhang, X., Cai, C., Gao, Z.: DVC: an end-to-end deep video compression framework. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 11006\u201311015 (2019)","DOI":"10.1109\/CVPR.2019.01126"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Lu, G., Ouyang, W., Xu, D., Zhang, X., Gao, Z., Sun, M.T.: Deep kalman filtering network for video compression artifact reduction. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01264-9_35"},{"key":"27_CR22","first-page":"1","volume":"PP","author":"G Lu","year":"2020","unstructured":"Lu, G., Zhang, X., Ouyang, W., Chen, L., Gao, Z., Xu, D.: An end-to-end learning framework for video compression. IEEE Trans. Pattern Anal. Mach. Intell. PP, 1 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Van Gool, L.: Conditional probability models for deep image compression. In: CVPR, p. 3, no. 2 (2018)","DOI":"10.1109\/CVPR.2018.00462"},{"key":"27_CR24","unstructured":"Minnen, D., Ball\u00e9, J., Toderici, G.D.: Joint autoregressive and hierarchical priors for learned image compression. In: Advances in Neural Information Processing Systems, pp. 10771\u201310780 (2018)"},{"key":"27_CR25","unstructured":"Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: ICML (2017)"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Rippel, O., Nair, S., Lew, C., Branson, S., Anderson, A.G., Bourdev, L.D.: Learned video compression. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, pp. 3453\u20133462. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00355"},{"issue":"9","key":"27_CR27","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1109\/TCSVT.2007.905532","volume":"17","author":"H Schwarz","year":"2007","unstructured":"Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H.264\/AVC standard. IEEE Trans. Circuits Syst. Video Technol. 17(9), 1103\u20131120 (2007)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"10","key":"27_CR28","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","volume":"40","author":"MJ Shensa","year":"1992","unstructured":"Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464\u20132482 (1992)","journal-title":"IEEE Trans. Signal Process."},{"issue":"5","key":"27_CR29","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/79.952804","volume":"18","author":"A Skodras","year":"2001","unstructured":"Skodras, A., Christopoulos, C., Ebrahimi, T.: The JPEG 2000 still image compression standard. IEEE Signal Process. Mag. 18(5), 36\u201358 (2001)","journal-title":"IEEE Signal Process. Mag."},{"issue":"12","key":"27_CR30","first-page":"1649","volume":"22","author":"GJ Sullivan","year":"2012","unstructured":"Sullivan, G.J., Ohm, J.R., Han, W.J., Wiegand, T., et al.: Overview of the high efficiency video coding (HEVC) standard. TCSVT 22(12), 1649\u20131668 (2012)","journal-title":"TCSVT"},{"key":"27_CR31","unstructured":"Theis, L., Shi, W., Cunningham, A., Husz\u00e1r, F.: Lossy image compression with compressive autoencoders. In: Proceedings of the 5th International Conference on Learning Representations, ICLR (2017)"},{"key":"27_CR32","unstructured":"Toderici, G., et al.: Variable rate image compression with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations, ICLR (2016)"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Toderici, G., et al.: Full resolution image compression with recurrent neural networks. In: CVPR, pp. 5435\u20135443 (2017)","DOI":"10.1109\/CVPR.2017.577"},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Liu, M.Y., Sun, D., Yang, M.H., Kautz, J.: Learning binary residual representations for domain-specific video streaming. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12259"},{"key":"27_CR35","doi-asserted-by":"crossref","unstructured":"Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), xviii\u2013xxxiv (1992)","DOI":"10.1109\/30.125072"},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: MCL-JCV: a JND-based H.264\/AVC video quality assessment dataset. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1509\u20131513. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532610"},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"27_CR38","unstructured":"Wang, Z., Simoncelli, E., Bovik, A., et al.: Multi-scale structural similarity for image quality assessment. In: ASILOMAR Conference on Signals systems and Computers, vol. 2, pp. 1398\u20131402. IEEE (2003). 1998"},{"issue":"7","key":"27_CR39","first-page":"560","volume":"13","author":"T Wiegand","year":"2003","unstructured":"Wiegand, T., Sullivan, G.J., Bjontegaard, G., Luthra, A.: Overview of the H.264\/AVC video coding standard. TCSVT 13(7), 560\u2013576 (2003)","journal-title":"TCSVT"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Singhal, N., Krahenbuhl, P.: Video compression through image interpolation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01237-3_26"},{"issue":"8","key":"27_CR41","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11263-018-01144-2","volume":"127","author":"T Xue","year":"2019","unstructured":"Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vision 127(8), 1106\u20131125 (2019)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58536-5_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:44:02Z","timestamp":1730594642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58536-5_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585358","9783030585365"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58536-5_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}