{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T20:25:50Z","timestamp":1767731150707,"version":"build-2238731810"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the development of information technology, multimedia has become a common information storage technology. The original information query technology has been difficult to adapt to the development of this new technology, so in order to be able to retrieve useful information in a large amount of multimedia information which has become a hot topic in the development of search technology, this paper takes the image in the multimedia information storage technology as the research object, uses the wavelet transform to divide the picture into the advantages of the low-frequency and high-frequency characteristics, and establishes the multimedia processing technology model based on the wavelet transform. The simulation results of face, vehicle, building, and landscape images show that different wavelet basis functions and different layers of images are decomposed, and the retrieval results and retrieval speed of images are different, When taking four layers of wavelet decomposition, the cubic b-spline wavelet as the wavelet basis function makes the classification result optimal, and the accuracy rate is 89.08%.<\/jats:p>","DOI":"10.1186\/s13640-018-0396-1","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T08:04:43Z","timestamp":1548749083000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Research on application of multimedia image processing technology based on wavelet transform"],"prefix":"10.1186","volume":"2019","author":[{"given":"Kun","family":"Sui","sequence":"first","affiliation":[]},{"given":"Hyung-Gi","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,29]]},"reference":[{"key":"396_CR1","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.5740\/jaoacint.17-0308","volume":"101","author":"S Ahmadi","year":"2018","unstructured":"S. Ahmadi, A. Manivarnosfaderani, B. Habibi, Motor oil classification using color histograms and pattern recognition techniques. J. AOAC Int. 101, 1967\u20131976 (2018)","journal-title":"J. AOAC Int."},{"key":"396_CR2","doi-asserted-by":"crossref","unstructured":"Liu H, Zhao F, Chaudhary V. Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation. Digital Signal Process. 76, 75-83 (2018)","DOI":"10.1016\/j.dsp.2018.02.005"},{"key":"396_CR3","first-page":"102251P","volume":"225","author":"L Li","year":"2017","unstructured":"L. Li, K. Liu, F. Cheng, An improved TLD with Harris corner and color moment. Proceedings of the Spie 225, 102251P (2017)","journal-title":"Proceedings of the Spie"},{"issue":"9","key":"396_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5120\/ijca2017913282","volume":"161","author":"V Vinayak","year":"2017","unstructured":"V. Vinayak, S. Jindal, CBIR system using color moment and color auto-Correlogram with block truncation coding. International Journal of Computer Applications 161(9), 1\u20137 (2017)","journal-title":"International Journal of Computer Applications"},{"issue":"2","key":"396_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1889\/1.1984851","volume":"1","author":"PGJ Barten","year":"2012","unstructured":"P.G.J. Barten, Effects of quantization and pixel structure on the image quality of color matrix displays. J. Soc. Inf. Disp. 1(2), 147\u2013153 (2012)","journal-title":"J. Soc. Inf. Disp."},{"key":"396_CR6","doi-asserted-by":"crossref","unstructured":"I.M. Stephanakis, G.C. Anastassopoulos, L. Iliadis. A self-organizing feature map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures. Neurocomputing 107, 97-107 (2013)","DOI":"10.1016\/j.neucom.2012.09.010"},{"issue":"4","key":"396_CR7","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neucom.2012.09.010","volume":"107","author":"IM Stephanakis","year":"2013","unstructured":"I.M. Stephanakis, G.C. Anastassopoulos, L.A. Iliadis, Self-organizing feature map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures. Neurocomputing 107(4), 97\u2013107 (2013)","journal-title":"Neurocomputing"},{"issue":"4","key":"396_CR8","first-page":"551","volume":"9","author":"D Chai","year":"1999","unstructured":"D. Chai, K.N. Ngan, Face segmentation using skin-color map in videophone applications. IEEE Trans Csvt 9(4), 551\u2013564 (1999)","journal-title":"IEEE Trans Csvt"},{"issue":"2","key":"396_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.jtbi.2016.02.038","volume":"397","author":"I Pantic","year":"2016","unstructured":"I. Pantic, Z. Nesic, J.P. Pantic, et al., Fractal analysis and gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla. J. Theor. Biol. 397(2), 61\u201367 (2016)","journal-title":"J. Theor. Biol."},{"key":"396_CR10","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.jtbi.2016.07.018","volume":"406","author":"I Pantic","year":"2016","unstructured":"I. Pantic, D. Dimitrijevic, D. Nesic, et al., Grey level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in chromatin architecture. J. Theor. Biol. 406, 124\u2013128 (2016)","journal-title":"J. Theor. Biol."},{"key":"396_CR11","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.jtbi.2016.07.018","volume":"406","author":"I Pantic","year":"2016","unstructured":"I. Pantic, D. Dimitrijevic, D. Nesic, et al., Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture. J. Theor. Biol. 406, 124\u2013128 (2016)","journal-title":"J. Theor. Biol."},{"issue":"12","key":"396_CR12","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/0031-3203(91)90143-S","volume":"24","author":"AK Jain","year":"1991","unstructured":"A.K. Jain, F. Farrokhnia, et al., Unsupervised texture segmentation using Gabor filters. Pattern Recogn. 24(12), 1167\u20131186 (1991)","journal-title":"Pattern Recogn."},{"key":"396_CR13","doi-asserted-by":"crossref","unstructured":"Jain A K, Farrokhnia F. Unsupervised texture segmentation using Gabor filters[C]\/\/ IEEE International Conference on Systems, Man and Cybernetics, 1990. Conference proceedings. IEEE, 2002:1167\u20131186","DOI":"10.1016\/0031-3203(91)90143-S"},{"issue":"10","key":"396_CR14","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TIP.2002.804262","volume":"11","author":"SE Grigorescu","year":"2002","unstructured":"S.E. Grigorescu, N. Petkov, P. Kruizinga, Comparison of texture features based on Gabor filters. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society 11(10), 1160\u20131167 (2002)","journal-title":"IEEE transactions on image processing: a publication of the IEEE Signal Processing Society"},{"key":"396_CR15","doi-asserted-by":"crossref","unstructured":"Navarro-Alarcon D, Liu Y H. Fourier-based shape servoing: a new feedback method to actively deform soft objects into desired 2-D image contours. IEEE Trans. Robot., 2018, PP(99):1\u20138","DOI":"10.1109\/TRO.2017.2765333"},{"issue":"5","key":"396_CR16","doi-asserted-by":"publisher","first-page":"1472","DOI":"10.1364\/AO.56.001472","volume":"56","author":"H Yun","year":"2017","unstructured":"H. Yun, B. Li, S. Zhang, Pixel-by-pixel absolute three-dimensional shape measurement with modified Fourier transform profilometry. Appl. Optics 56(5), 1472 (2017)","journal-title":"Appl. Optics"},{"issue":"1","key":"396_CR17","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/0031-3203(93)90098-H","volume":"26","author":"J Flusser","year":"1993","unstructured":"J. Flusser, T. Suk, Pattern recognition by affine moment invariant. Pattern Recogn. 26(1), 167\u2013174 (1993)","journal-title":"Pattern Recogn."},{"issue":"1","key":"396_CR18","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TMI.2010.2064333","volume":"30","author":"D Marin","year":"2011","unstructured":"D. Marin, A. Aquino, M.E. Gegundezarias, et al., A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"396_CR19","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1109\/83.336250","volume":"3","author":"MR Banham","year":"2016","unstructured":"M.R. Banham, N.P. Galatsanos, H.L. Gonzalez, et al., Multichannel restoration of single channel images using a wavelet-based subband decomposition. IEEE Trans. Image Process. 3(6), 821\u2013833 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"396_CR20","doi-asserted-by":"publisher","first-page":"2585","DOI":"10.1049\/iet-gtd.2015.0911","volume":"10","author":"H Shao","year":"2016","unstructured":"H. Shao, X. Deng, F. Cui, Short-term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China. Iet Generation Transmission & Distribution 10(11), 2585\u20132592 (2016)","journal-title":"Iet Generation Transmission & Distribution"},{"issue":"10","key":"396_CR21","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1049\/iet-ipr.2015.0818","volume":"10","author":"Y Guo","year":"2016","unstructured":"Y. Guo, B.Z. Li, Blind image watermarking method based on linear canonical wavelet transform and QR decomposition. IET Image Process. 10(10), 773\u2013786 (2016)","journal-title":"IET Image Process."},{"issue":"8","key":"396_CR22","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1049\/iet-cvi.2015.0486","volume":"10","author":"KR Singh","year":"2017","unstructured":"K.R. Singh, S. Chaudhury, Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition. IET Comput. Vis. 10(8), 780\u2013787 (2017)","journal-title":"IET Comput. Vis."},{"issue":"4","key":"396_CR23","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/78.668573","volume":"46","author":"WW Boles","year":"1998","unstructured":"W.W. Boles, B. Boashash, A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4), 1185\u20131188 (1998)","journal-title":"IEEE Trans. Signal Process."},{"issue":"3","key":"396_CR24","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1006\/gmip.1995.1022","volume":"57","author":"H Li","year":"1995","unstructured":"H. Li, B.S. Manjunath, S.K. Mitra, Multisensor image fusion using the wavelet transform. Graphical models and image processing 57(3), 235\u2013245 (1995)","journal-title":"Graphical models and image processing"},{"issue":"4","key":"396_CR25","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/83.242353","volume":"2","author":"T Chang","year":"1993","unstructured":"T. Chang, C.-C.J. Kuo, Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429\u2013441 (1993)","journal-title":"IEEE Trans. Image Process."}],"updated-by":[{"DOI":"10.1186\/s13640-023-00603-2","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000}}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-018-0396-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13640-018-0396-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-018-0396-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T10:07:16Z","timestamp":1678961236000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-018-0396-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,29]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["396"],"URL":"https:\/\/doi.org\/10.1186\/s13640-018-0396-1","relation":{},"ISSN":["1687-5281"],"issn-type":[{"value":"1687-5281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,29]]},"assertion":[{"value":"23 September 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1186\/s13640-023-00603-2","URL":"https:\/\/doi.org\/10.1186\/s13640-023-00603-2","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"301 Art Center Chung-Ang University 221 Heukseok-dong Dongjak-gu, Seoul, 156\u2013756 Korea.Kun Sui was born in Qingdao, Shandong, P.R. China, in 1982. Doctor of Technology Art, Lecturer. Graduated from the Korea Dong Yang University in 2009. Worked in Qingdao Agricultural University. His research interests include New Media Art and digital image processing.*Author for correspondence:Hyung-Gi Kim, was born in Korea, in 1960.Doctor of Technology Art, Professor. Graduated from the Soongsil University in 2009. Worked in Graduate school of Advanced Imaging Science, Multimedia and Film Chung-Ang University, Seoul, Korea. He has held eleven successful solo Media Art Exhibitions and participated in many group exhibitions. His research focuses on 3D display systems, projection mapping, kinetic art, interactive media art, and media performance.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Authors\u2019 information"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"24"}}