{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:13:02Z","timestamp":1742958782653,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030447502"},{"type":"electronic","value":"9783030447519"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-44751-9_22","type":"book-chapter","created":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T14:06:10Z","timestamp":1585663570000},"page":"249-258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Compressive-Sensing Based Codec of the Y Color Component for Point Cloud"],"prefix":"10.1007","author":[{"given":"Weiwei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Hui","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,1]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Garrote, L., Rosa, J., Paulo, J., et al.: 3D point cloud downsampling for 2D indoor scene modelling in mobile robotics. In: IEEE International Conference on Autonomous Robot Systems & Competitions. IEEE (2017)","DOI":"10.1109\/ICARSC.2017.7964080"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Mousavian, A., Anguelov, D., Flynn, J., et al.: 3D bounding box estimation using deep learning and geometry (2016)","DOI":"10.1109\/CVPR.2017.597"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TCSVT.2016.2543039","volume":"27","author":"R Mekuria","year":"2016","unstructured":"Mekuria, R., Blom, K., Cesar, P.: Design, implementation and evaluation of a point cloud codec for tele-immersive video. IEEE Trans. Circuits Syst. Video Technol. 27, 828\u2013842 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Du, Y., ShangGuan, W., Chai, L.: Particle filter based object tracking of 3D sparse point clouds for autopilot. In: 2018 Chinese Automation Congress (CAC), Xi\u2019an, China, pp. 1102\u20131107 (2018)","DOI":"10.1109\/CAC.2018.8623097"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: The research of chemical plant monitoring base on the internet of things and 3D visualization technology. In: 2013 IEEE International Conference on Information and Automation (ICIA), Yinchuan, pp. 860\u2013864 (2013)","DOI":"10.1109\/ICInfA.2013.6720414"},{"key":"22_CR6","unstructured":"Schnabel, R., Klein, R.: Octree-based point-cloud compression. In: Eurographics. Eurographics Association (2006)"},{"issue":"4","key":"22_CR7","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TCSVT.2016.2543039","volume":"27","author":"R Mekuria","year":"2017","unstructured":"Mekuria, R., Blom, K., Cesar, P.: Design, implementation, and evaluation of a point cloud codec for tele-immersive video. IEEE Trans. Circuits Syst. Video Technol. 27(4), 828\u2013842 (2017)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"22_CR8","unstructured":"Tu, C., Takeuchi, E., Miyajima, C., Takeda, K.: Compressing continuous point cloud data using image compression methods. In: Proceedings of the IEEE Intelligent Transportation Systems (ITSC), Rio de Janeiro, pp. 1712\u20131719 (2016)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Cui, L., Xu, H., Jang, E.S.: Hybrid color attribute compression for point cloud data. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, pp. 1273\u20131278 (2017)","DOI":"10.1109\/ICME.2017.8019426"},{"issue":"2","key":"22_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/TIT.2005.862083","volume":"52","author":"EJ Cand\u00e8s","year":"2006","unstructured":"Cand\u00e8s, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489\u2013509 (2006)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"4","key":"22_CR11","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"D Donoho","year":"2006","unstructured":"Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289\u20131306 (2006)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Cand\u00e8s, E.: Compressive sampling. In: International Congress of Mathematics, Madrid, Spain, pp. 1433\u20131452 (2006)","DOI":"10.4171\/022-3\/69"},{"issue":"4","key":"22_CR13","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/MSP.2007.4286571","volume":"24","author":"R Baraniuk","year":"2007","unstructured":"Baraniuk, R.: Compressive sensing. IEEE Signal Process. Mag. 24(4), 118\u2013121 (2007)","journal-title":"IEEE Signal Process. Mag."},{"issue":"9","key":"22_CR14","doi-asserted-by":"publisher","first-page":"5117","DOI":"10.1109\/TIT.2016.2556683","volume":"62","author":"CA Metzler","year":"2014","unstructured":"Metzler, C.A., Maleki, A., Baraniuk, R.G.: From denoising to compressed sensing. IEEE Trans. Inf. Theory 62(9), 5117\u20135144 (2014)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"12","key":"22_CR15","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736\u20133745 (2006)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"22_CR16","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1587\/transfun.E99.A.2095","volume":"99\u2013A","author":"L Ma","year":"2016","unstructured":"Ma, L., Bai, H., Zhang, M., Zhao, Y.: Edge-based adaptive sampling for image block compressive sensing. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 99\u2013A(11), 2095\u20132098 (2016)","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Bai, H., Zhao, Y.: Image reconstruction from patch compressive sensing measurements. In: 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM). IEEE Computer Society (2018)","DOI":"10.1109\/BigMM.2018.8499088"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Athira, V., George, S.N., Deepthi, P.P.: A novel encryption method based on compressive sensing. In: International Multi-conference on Automation. IEEE (2013)","DOI":"10.1109\/iMac4s.2013.6526421"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Baselice, F., Ferraioli, G., Matuozzo, G., et al.: Compressive sensing for in depth focusing in 3D automotive imaging radar. In: 2015 3rd International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing (CoSeRa). IEEE (2015)","DOI":"10.1109\/CoSeRa.2015.7330266"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Bai, H., Wang, A., Zhang, M.: Compressive sensing for DCT image. In: International Conference on Computational Aspects of Social Networks. IEEE (2010)","DOI":"10.1109\/CASoN.2010.92"},{"issue":"8","key":"22_CR21","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1109\/TIP.2006.875207","volume":"15","author":"DWD Wang","year":"2006","unstructured":"Wang, D.W.D., Zhang, L.Z.L., Vincent, A., et al.: Curved wavelet transform for image coding. IEEE Trans. Image Process. 15(8), 2413\u20132421 (2006)","journal-title":"IEEE Trans. Image Process."},{"key":"22_CR22","unstructured":"Cheng, G.Q., Cheng, L.Z.: A new image compression via adaptive wavelet transform. In: International Conference on Wavelet Analysis & Pattern Recognition. IEEE (2007)"},{"issue":"11","key":"22_CR23","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Signal Process."},{"issue":"6","key":"22_CR24","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/LSP.2013.2258912","volume":"20","author":"SK Sahoo","year":"2013","unstructured":"Sahoo, S.K., Makur, A.: Dictionary training for sparse representation as generalization of K-means clustering. IEEE Signal Process. Lett. 20(6), 587\u2013590 (2013)","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","IoT as a Service"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-44751-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T23:34:36Z","timestamp":1585697676000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-44751-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030447502","9783030447519"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-44751-9_22","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"1 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IoTaaS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Internet of Things as a Service","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iotaas2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iotaas2019.eai-conferences.org\/","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":"Confy","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","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":"56","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":"53% - 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":"3.73","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}