{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:26Z","timestamp":1743033626713,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030601133"},{"type":"electronic","value":"9783030601140"}],"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\/978-3-030-60114-0_16","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T13:04:26Z","timestamp":1601643866000},"page":"234-247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Is It Possible to Predict Human Perception of Video Quality? The Assessment of Sencogi Quality Metric"],"prefix":"10.1007","author":[{"given":"Maria Laura","family":"Mele","sequence":"first","affiliation":[]},{"given":"Silvia","family":"Colabrese","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Calabria","sequence":"additional","affiliation":[]},{"given":"Christiaan Erik","family":"Rijnders","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,3]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Chikkerur, S., Sundaram, V., Reisslein, M., Karam, L.J.: Objective video quality assessment methods: a classification, review, and performance comparison (2011). http:\/\/dx.doi.org\/10.1109\/tbc.2011.2104671","DOI":"10.1109\/TBC.2011.2104671"},{"key":"16_CR2","unstructured":"International Telecommunication Union \u2013 ITU: Recommendation ITU-R BT.500-14 (10\/2019). Methodologies for the subjective assessment of the quality of television images. BT Series. Broadcasting service (television), October 2019"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kwong, S., Wang, S.: Machine learning based video coding optimizations: a survey (2020). http:\/\/dx.doi.org\/10.1016\/j.ins.2019.07.096","DOI":"10.1016\/j.ins.2019.07.096"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Staelens, N., et al.: Assessing quality of experience of IPTV and video on demand services in real-life environments (2010). http:\/\/dx.doi.org\/10.1109\/tbc.2010.2067710","DOI":"10.1109\/TBC.2010.2067710"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity (2004). http:\/\/dx.doi.org\/10.1109\/tip.2003.819861","DOI":"10.1109\/TIP.2003.819861"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bovik, A.C.: Modern image quality assessment (2006). http:\/\/dx.doi.org\/10.2200\/s00010ed1v01y200508ivm003","DOI":"10.1007\/978-3-031-02238-8"},{"key":"16_CR7","unstructured":"Li, Z., Aaron, A., Katsavounidis, I., Moorthy, A., Manohara, M.: Toward a practical perceptual video quality metric. Netflix Tech Blog. 6, 2 (2016)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Bosse, S., Maniry, D., Muller, K.-R., Wiegand, T., Samek, W.: Deep neural networks for no-reference and full-reference image quality assessment (2018). http:\/\/dx.doi.org\/10.1109\/tip.2017.2760518","DOI":"10.1109\/TIP.2017.2760518"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment (2014). http:\/\/dx.doi.org\/10.1109\/cvpr.2014.224","DOI":"10.1109\/CVPR.2014.224"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gao, X., He, L., Lu, W., He, R.: Objective video quality assessment combining transfer learning with CNN (2019). http:\/\/dx.doi.org\/10.1109\/tnnls.2018.2890310","DOI":"10.1109\/TNNLS.2018.2890310"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment (2012). http:\/\/dx.doi.org\/10.1109\/cvpr.2012.6247789","DOI":"10.1109\/CVPR.2012.6247789"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"2971","DOI":"10.1109\/TIP.2015.2436332","volume":"24","author":"F Shao","year":"2015","unstructured":"Shao, F., Li, K., Lin, W., Jiang, G., Yu, M., Dai, Q.: Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties. IEEE Trans. Image Process. 24, 2971\u20132983 (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2436332","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2929433","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Zhang, H., Yu, M., Kwong, S., Ho, Y.-S.: Sparse representation based video quality assessment for synthesized 3D videos. IEEE Trans. Image Process. (2019). https:\/\/doi.org\/10.1109\/TIP.2019.2929433","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR14","unstructured":"Rijnders, C.E.: U.S. Patent Application No. 15\/899,331 (2018)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: Study of subjective and objective quality assessment of video (2010). http:\/\/dx.doi.org\/10.1109\/tip.2010.2042111","DOI":"10.1109\/TIP.2010.2042111"},{"key":"16_CR16","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.visres.2012.06.001","volume":"65","author":"J Shen","year":"2012","unstructured":"Shen, J., Itti, L.: Top-down influences on visual attention during listening are modulated by observer sex. Vis. Res. 65, 62\u201376 (2012). https:\/\/doi.org\/10.1016\/j.visres.2012.06.001","journal-title":"Vis. Res."},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Rimac-Drlje, S., Vranje\u0161, M., \u017dagar, D.: Foveated mean squared error\u2014a novel video quality metric (2016). http:\/\/dx.doi.org\/10.1007\/s11042-009-0442-1","DOI":"10.1007\/s11042-009-0442-1"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Vranje\u0161, M., Rimac-Drlje, S., Grgi\u0107, K.: Review of objective video quality metrics and performance comparison using different databases. Sign. Process.-Image Commun. (2012). http:\/\/dx.doi.org\/10.1016\/j.image.2012.10.003","DOI":"10.1016\/j.image.2012.10.003"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Corriveau, P., Webster, A.: The video quality experts group: evaluates objective methods of video image quality assessment (1998). http:\/\/dx.doi.org\/10.5594\/m00304","DOI":"10.5594\/M00304"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Mele, M.L., Millar, D., Rijnders, C.E.: The web-based subjective quality assessment of an adaptive image compression plug-in (2017). http:\/\/dx.doi.org\/10.5220\/0006226401330137","DOI":"10.5220\/0006226401330137"},{"key":"16_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/978-3-319-91244-8_43","volume-title":"Human-Computer Interaction. Interaction in Context","author":"ML Mele","year":"2018","unstructured":"Mele, M.L., Millar, D., Rijnders, C.E.: Sencogi spatio-temporal saliency: a new metric for predicting subjective video quality on mobile devices. In: Kurosu, M. (ed.) HCI 2018. LNCS, vol. 10902, pp. 552\u2013564. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91244-8_43"},{"key":"16_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/978-3-319-58071-5_27","volume-title":"Human\u2013Computer Interaction. User Interface Design, Development and Multimodality","author":"ML Mele","year":"2017","unstructured":"Mele, M.L., Millar, D., Rijnders, C.E.: Using spatio-temporal saliency to predict subjective video quality: a new high-speed objective assessment metric. In: Kurosu, M. (ed.) HCI 2017. LNCS, vol. 10271, pp. 353\u2013368. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58071-5_27"},{"key":"16_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/978-3-030-22643-5_26","volume-title":"Human-Computer Interaction. Recognition and Interaction Technologies","author":"ML Mele","year":"2019","unstructured":"Mele, M.L., Colabrese, S., Calabria, L., Millar, D., Rijnders, C.E.: The assessment of sencogi: a visual complexity model predicting visual fixations. In: Kurosu, M. (ed.) HCII 2019. LNCS, vol. 11567, pp. 332\u2013347. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22643-5_26"}],"container-title":["Lecture Notes in Computer Science","HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60114-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:03:44Z","timestamp":1727827424000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60114-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030601133","9783030601140"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60114-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","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":"19 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}