{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:16:09Z","timestamp":1773389769151,"version":"3.50.1"},"reference-count":30,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>Visual perception principle of watching video is crucial in ensuring video works accurately and effectively grasped by audience. This article proposes an investigation into the efficiency of human visual perception on video clips considering exposure duration. The study focused on the correlation between the video shot duration and the subject?s perception of visual content. The subjects? performances were captured as perceptual scores on the testing videos by watching time-regulated clips and taking questionnaire. The statistical results show that three-second duration for each video shot is necessary for audience to grasp the main visual information. The data also indicate gender differences in perceptual procedure and attention focus. The findings can help for manipulating clip length in video editing, both via AI tools and manually, maintaining perception efficiency as possible in limited duration. This method is significant for its structured experiment involving subjects? quantified performances, which is different from AI methods of unaccountable.<\/jats:p>","DOI":"10.2298\/csis220919017s","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T08:28:09Z","timestamp":1677659289000},"page":"879-892","source":"Crossref","is-referenced-by-count":2,"title":["The duration threshold of video content observation: An experimental investigation of visual perception efficiency"],"prefix":"10.2298","volume":"20","author":[{"given":"Jianping","family":"Song","sequence":"first","affiliation":[{"name":"School of Art and Design, Zhejiang A&F University, Hangzhou, China"}]},{"given":"Tianran","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Dongguan Polytechnic, Dongguan, China"}]},{"given":"Guosheng","family":"Hu","sequence":"additional","affiliation":[{"name":"Design Innovation Center, China Academy of Art, Hangzhou, China"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Gkalelis, N., Goulas, A., Galanopoulos, D., Mezaris, V. Object Graphs: Using Objects and a Graph Convolutional Network for the Bottom-up Recognition and Explanation of Events in Video. Computer Vision and Pattern Recognition, IEEE. (2021)","DOI":"10.1109\/CVPRW53098.2021.00376"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Schwenzow, J., Hartmann, J., Schikowsky, A., Heitmann, M. Understanding videos at scale: How to extract insights for business research. Journal of Business Research 123:367-379. (2021)","DOI":"10.1016\/j.jbusres.2020.09.059"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yu, X., Lei, X., Wu, C. A Novel Deep LeNet-5 Convolutional Neural Network Model for Image Recognition. Computer Science and Information Systems 19(3):1463-1480. (2022)","DOI":"10.2298\/CSIS220120036Z"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Conrad, M., Cin, M.D., Marr, D. Approaches to biological information processing. Science 190:875-876. (1975)","DOI":"10.1126\/science.190.4217.875"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Marr, D. Early processing of visual information, Philosophical Transactions of the Royal Society of London. Biological Sciences 275(942):483-519. (1976)","DOI":"10.1098\/rstb.1976.0090"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Johansson, G. Visual perception of biological motion and a model for its analysis. Atten. Percept. Psychophys 14:201-211. (1973)","DOI":"10.3758\/BF03212378"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Johansson, G. Spatio-temporal differentiation and integration in visual motion perception. Psychological Research 38(4):379-393. (1976)","DOI":"10.1007\/BF00309043"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Albright, T.D., Stoner, G.R. Visual motion perception. Proceedings of the National Academy of Sciences of the United States of America 92(7):2433-2440. (1995)","DOI":"10.1073\/pnas.92.7.2433"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Chun, M.M. Contextual cueing of visual attention. Trends Cognit Sci, 4(5):0-178. (2000)","DOI":"10.1016\/S1364-6613(00)01476-5"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Watt, R.J. Scanning from coarse to fine spatial scales in the human visual system after the onset of a stimulus. Journal of the Optical Society of America A-optics Image Science & Vision 4(10):2006-2021. (1987)","DOI":"10.1364\/JOSAA.4.002006"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Bicanski A., Burgess, N. A Computational Model of Visual Recognition Memory via Grid Cells. Current Biology 29(6):979-990. (2019)","DOI":"10.1016\/j.cub.2019.01.077"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Rybak, I.A., Golovan, A.V., Gusakova, V.I. Behavioral model of visual perception and recognition. Proceedings of SPIE - The International Society for Optical Engineering 1913:548-560. (1993)","DOI":"10.1117\/12.152729"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Thorpe, S., Fize, D., Marlot, C. Speed of processing in the human visual system. Nature 381:520-522. (1996)","DOI":"10.1038\/381520a0"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"uB\u00fclthoff, I., Newell, F.N. The role of familiarity in the recognition of static and dynamic objects. Progress in Brain Research 154:315-325. (2006)","DOI":"10.1016\/S0079-6123(06)54017-8"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Fabre-Thorpe, M., Delorme, A., Marlot, C., Thorpe, S. A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. Journal of Cognitive Neuroscience 13(2):171-180. (2001)","DOI":"10.1162\/089892901564234"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Zhou, C., Lorist, M.M., Math\u00f4t, S. Categorical bias as a crucial parameter in visual working memory: The effect of memory load and retention interval. Cortex 154:311-321. (2022)","DOI":"10.1016\/j.cortex.2022.05.007"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Zafar, B., Ashraf, R., Ali, N., Ahmed, M., Jabbar, S., Naseer, K., Ahmad, A., Jeon, G. Intelligent Image Classification-Based on Spatial Weighted Histograms of Concentric Circles. Computer Science and Information Systerms 15(3):615-633. (2018)","DOI":"10.2298\/CSIS180105025Z"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Posner, M.I., Petersen, S.E. The attention system of the human brain. Annual Review of Neuroscience 13(1):25-42. (1990)","DOI":"10.1146\/annurev.neuro.13.1.25"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Kastner, S., Ungerleider, L.G. Mechanisms of visual attention in the human cortex. Annual review of neuroscience 23:315-341. (2000)","DOI":"10.1146\/annurev.neuro.23.1.315"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Intraub, H. The representation of visual scenes. Trends in Cognitive Sciences 1(6):0-222. (1997)","DOI":"10.1016\/S1364-6613(97)01067-X"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Bar, M., Kassam, K.S., Ghuman, A.S., Boshyan, J., Schmidt, A.M., Dale, A.M., Hamalainen, M.S., Marinkovic, K., Schacter, D.L., Rosen, B.R., Halgren, E. Top-down facilitation of visual recognition. Proc Natl Acad Sci USA 103(2):449-54. (2006)","DOI":"10.1073\/pnas.0507062103"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Fan, S., Koenig, B.L., Zhao, Q., Kankanhalli, M.S. A Deeper Look at Human Visual Perception of Images. SN Computer Science 1(1):58. (2020)","DOI":"10.1007\/s42979-019-0061-5"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wen, X., Whang, M. Recognition of Emotion According to the Physical Elements of the Video. Sensors 20(3):648. (2020)","DOI":"10.3390\/s20030649"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Privitera, C.M., Stark, L.W. Algorithms for defining visual region-of-interesting: comparison with eye fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(9):970-982. (2000)","DOI":"10.1109\/34.877520"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Baluch, F., Itti, L. Mechanisms of top-down attention. Trends in Neurosciences 34(4):210-224. (2011)","DOI":"10.1016\/j.tins.2011.02.003"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Netravali, A.N., Haskell, B.G. Digital Pictures: Representation and Compression. New York: Plenum (1988)","DOI":"10.1007\/978-1-4684-1294-9"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4):600-612. (2004)","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Peter, R.J., Iyer, A., Koch, C., Itti, L. Components of bottom-up gaze allocation in natural scenes. J. Vison 5(8):692-692. (2005)","DOI":"10.1167\/5.8.692"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Joubert, O.R., Rousselet, G.A., Fize, D., Fabre-Thorpe, M. Processing scene context: fast categorization and object interference. Vision Research 47:3286-3297. (2007)","DOI":"10.1016\/j.visres.2007.09.013"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Macknik, S.L., Livingstone, M.S. Neuronal correlates of visibility and invisibility in the primate visual system. Nature Neuroscience 1(2):144-149. 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