{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:14:44Z","timestamp":1742969684014,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031204357"},{"type":"electronic","value":"9783031204364"}],"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\/978-3-031-20436-4_5","type":"book-chapter","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:02:55Z","timestamp":1668988975000},"page":"47-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Subjective Video Quality Assessment Based on Active Learning and Clustering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7861-629X","authenticated-orcid":false,"given":"Xiaochen","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-5563","authenticated-orcid":false,"given":"Wei","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1375-0081","authenticated-orcid":false,"given":"Wenbo","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6574-2601","authenticated-orcid":false,"given":"Mario","family":"Di Mauro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2773-4421","authenticated-orcid":false,"given":"Antonio","family":"Liotta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"5_CR1","unstructured":"ITU-T, Recommendation P.913. https:\/\/www.itu.int\/rec\/T-REC-P.913-202106-I\/en. Accessed 13 June 2021"},{"issue":"4","key":"5_CR2","volume":"10","author":"A Smailagic","year":"2020","unstructured":"Smailagic, A., Costa, P., Gaudio, A., et al.: O-MedAL: Online active deep learning for medical image analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. 10(4), e1353 (2020)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Disc."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Hou, G., Li, Y., Yang, H., et al.: UID2021: an underwater image dataset for evaluation of no-reference quality assessment metrics. arXiv preprint arXiv:2204.08813 (2022)","DOI":"10.1145\/3578584"},{"issue":"6","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/TIP.2010.2042111","volume":"19","author":"K Seshadrinathan","year":"2010","unstructured":"Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: Study of subjective and objective quality assessment of video. IEEE Trans. Image Process. 19(6), 1427\u20131441 (2010)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"5_CR5","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1109\/TIP.2018.2869673","volume":"28","author":"Z Sinno","year":"2019","unstructured":"Sinno, Z., Bovik, A.C.: Large-scale study of perceptual video quality. IEEE Trans. Image Process. 28(2), 612\u2013627 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Hosu, V., et al.: The Konstanz natural video database (KoNViD-1k). In: 9th International Conference on Quality of Multimedia Experience (QoMEX), Erfurt, Germany, pp.1\u20136. IEEE (2017)","DOI":"10.1109\/QoMEX.2017.7965673"},{"issue":"12","key":"5_CR7","doi-asserted-by":"publisher","first-page":"31723","DOI":"10.3390\/s151229882","volume":"15","author":"JM Moreno-Rold\u00e1n","year":"2015","unstructured":"Moreno-Rold\u00e1n, J.M., Luque-Nieto, M.\u00c1., Poncela, J., et al.: Subjective quality assessment of underwater video for scientific applications. Sensors 15(12), 31723\u201331737 (2015)","journal-title":"Sensors"},{"issue":"9","key":"5_CR8","first-page":"1787","volume":"25","author":"W Song","year":"2020","unstructured":"Song, W., Liu, S.M., Huang, D.M., Wang, W.J., Wang, J.: Non-reference underwater video quality assessment method for small size samples. J. Image Graph. 25(9), 1787\u20131799 (2020). (in Chinese)","journal-title":"J. Image Graph."},{"issue":"8","key":"5_CR9","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.image.2012.01.004","volume":"27","author":"V Menkovski","year":"2012","unstructured":"Menkovski, V., Liotta, A.: Adaptive psychometric scaling for video quality assessment. Signal Process. Image Commun. 27(8), 788\u2013799 (2012)","journal-title":"Signal Process. Image Commun."},{"key":"5_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102062","volume":"71","author":"S Budd","year":"2021","unstructured":"Budd, S., Robinson, E.C., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal. 71, 102062 (2021)","journal-title":"Med. Image Anal."},{"issue":"12","key":"5_CR11","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1109\/TMM.2018.2831639","volume":"20","author":"HS Chang","year":"2018","unstructured":"Chang, H.S., Hsu, C.F., Ho\u00dffeld, T., Chen, K.T.: Active learning for crowdsourced QoE modeling. IEEE Trans. Multimedia 20(12), 3337\u20133352 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"5_CR12","unstructured":"Ling, S.Y., Li, J., Perrin A.F., et al.: Strategy for boosting pair comparison and improving quality assessment accuracy. arXiv preprint arXiv:2010.00370v1"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2019.04.070","volume":"358","author":"L Coletta","year":"2019","unstructured":"Coletta, L., Ponti, M., Hruschka, E.R., et al.: Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing 358, 150\u2013165 (2019)","journal-title":"Neurocomputing"},{"key":"5_CR14","doi-asserted-by":"publisher","first-page":"13250","DOI":"10.1007\/s10489-021-03139-y","volume":"52","author":"XY Yan","year":"2022","unstructured":"Yan, X.Y., Nazmi, S., Gebru, B., et al.: A clustering-based active learning method to query informative and representative samples. Appl. Intell. 52, 13250\u201313267 (2022). https:\/\/doi.org\/10.1007\/s10489-021-03139-y","journal-title":"Appl. Intell."},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"19547","DOI":"10.1007\/s00521-022-07376-1","volume":"34","author":"G Drakopoulos","year":"2022","unstructured":"Drakopoulos, G., Giannoukou, I., Mylonas, P., et al.: Self-organizing maps for cultural content delivery. Neural Comput. Appl. 34, 19547\u201319564 (2022). https:\/\/doi.org\/10.1007\/s00521-022-07376-1","journal-title":"Neural Comput. Appl."},{"issue":"12","key":"5_CR16","doi-asserted-by":"publisher","first-page":"6062","DOI":"10.1109\/TIP.2015.2491020","volume":"24","author":"M Yang","year":"2015","unstructured":"Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24(12), 6062\u20136071 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/JOE.2015.2469915","volume":"41","author":"K Panetta","year":"2016","unstructured":"Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Oceanic Eng. 41(3), 541\u2013551 (2016)","journal-title":"IEEE J. Oceanic Eng."},{"issue":"12","key":"5_CR18","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A., Bovik, A.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695\u20134708 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"5_CR19","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1109\/TIP.2014.2299154","volume":"23","author":"MA Saad","year":"2014","unstructured":"Saad, M.A., Bovik, A.C., Charrier, C.: Blind prediction of natural video quality. IEEE Trans. Image Process. 23(3), 1352\u20131365 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Robitza, W., Ramachandra Rao, R., G\u00f6ring S., Raake, A.: Impact of spatial and temporal information on video quality and compressibility. In: 13th International Conference on Quality of Multimedia Experience (QoMEX), Montreal, QC, Canada, pp. 65\u201368. IEEE (2021)","DOI":"10.1109\/QoMEX51781.2021.9465452"}],"container-title":["Lecture Notes in Computer Science","Advances in Mobile Computing and Multimedia Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20436-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T04:52:59Z","timestamp":1678683179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20436-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031204357","9783031204364"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20436-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MoMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Mobile Computing and Multimedia Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"momm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iiwas.org\/conferences\/momm2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34","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":"8","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":"8","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":"24% - 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":"4","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":"5","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)"}}]}}