{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:12:19Z","timestamp":1760235139568,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.<\/jats:p>","DOI":"10.3390\/e23081002","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:51:07Z","timestamp":1627854667000},"page":"1002","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Adaptive Rate Blocked Compressive Sensing Method for Video"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3872-1036","authenticated-orcid":false,"given":"Jianming","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3637-2565","authenticated-orcid":false,"given":"Jianhua","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Candes, E.J. (2006). Compressive sampling. Proc. Int. Congr. Math, 1433\u20131452.","DOI":"10.4171\/022-3\/69"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5406","DOI":"10.1109\/TIT.2006.885507","article-title":"Near-optimal signal recovery from random projections: Universal encoding strategies?","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2007.914730","article-title":"Single-pixel imaging via compressive sampling","volume":"25","author":"Duarte","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"562840","DOI":"10.1155\/2015\/562840","article-title":"Compressive-sensing-based video codec by autoregressive prediction and adaptive residual recovery","volume":"11","author":"Li","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Belyaev, E. (2020, January 25\u201328). Compressive sensed video coding having JPEG compatibility. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9190857"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Do, T.T., Chen, Y.D., Nguyen, T., Nguyen, N., Gan, L., and Tran, T.D. (2009, January 18\u201320). Distributed compressed video sensing. Proceedings of the 2009 43rd Annual Conference on Information Sciences and Systems, Baltimore, MD, USA.","DOI":"10.1109\/CISS.2009.5054678"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1007\/s11042-017-4345-2","article-title":"Efficient compressed sensing based object detection system for video surveillance application in WMSN","volume":"77","author":"Nandhini","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1109\/TSP.2014.2304917","article-title":"Sub-Nyquist radar via Doppler focusing","volume":"62","author":"Ilan","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1109\/TMI.2010.2085084","article-title":"Compressed-Sensing MRI with random encoding","volume":"30","author":"Haldar","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Asif, M.S., Fernandes, F., and Romberg, J. (2013, January 3\u20136). Low-complexity video compression and compressive sensing. Proceedings of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2013.6810345"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1561\/2000000033","article-title":"Block-based compressed sensing of images and video","volume":"4","author":"Fowler","year":"2012","journal-title":"Found. Trends Signal Process."},{"key":"ref_13","unstructured":"Gan, L. (2007, January 1\u20134). Block compressed sensing of natural images. Proceedings of the 2007 15th International Conference on Digital Signal Processing, Cardiff, UK."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1049\/iet-ipr.2019.0661","article-title":"Compressive sensed video recovery via iterative thresholding with random transforms","volume":"14","author":"Belyaev","year":"2020","journal-title":"IET Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TIT.2017.2653802","article-title":"Adaptive compressed sensing for support recovery of structured sparse sets","volume":"63","author":"Castro","year":"2017","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2422","DOI":"10.1109\/JSYST.2015.2447282","article-title":"Adaptive compressed spectrum sensing based on cross validation in WideBand cognitive radio system","volume":"11","author":"Qin","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/TBCAS.2015.2497304","article-title":"Ultra-low power dynamic knob in adaptive compressed sensing towards biosignal dynamics","volume":"10","author":"Wang","year":"2016","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TIP.2011.2177989","article-title":"Adaptive compressed sensing recovery utilizing the property of signal\u2019s autocorrelations","volume":"21","author":"Fu","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/TIP.2011.2163520","article-title":"Model-assisted adaptive recovery of compressed sensing with imaging applications","volume":"21","author":"Wu","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/JBHI.2016.2531182","article-title":"Adaptive dictionary reconstruction for compressed sensing of ECG signals","volume":"21","author":"Craven","year":"2017","journal-title":"IEEE. J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TGRS.2013.2245509","article-title":"Compressed sensing-based inpainting of aqua moderate resolution imaging spectroradiometer band 6 using adaptive spectrum-weighted sparse Bayesian dictionary learning","volume":"52","author":"Shen","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1109\/TSP.2012.2225054","article-title":"Task-driven adaptive statistical compressive sensing of Gaussian mixture models","volume":"61","author":"Yu","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1109\/TIP.2015.2456425","article-title":"Adaptive-rate compressive sensing using side information","volume":"24","author":"Warnell","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5077","DOI":"10.1109\/TIP.2016.2601444","article-title":"Compressive estimation and imaging based on autoregressive models","volume":"25","author":"Testa","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s10043-018-0408-5","article-title":"Adaptive temporal compressive sensing for video with motion estimation","volume":"25","author":"Wang","year":"2018","journal-title":"Opt. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1049\/iet-ipr.2019.0116","article-title":"Compressive domain spatial\u2013temporal difference saliency-based realtime adaptive measurement method for video recovery","volume":"13","author":"Li","year":"2019","journal-title":"IET Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, J., and Chen, J. (2019, January 6). Adaptive-rate compressive sensing for monitoring video based on fast sparsity estimation. Proceedings of the 2nd International Conference on Information Technologies and Electrical Engineering, Changsha, China.","DOI":"10.1145\/3386415.3386961"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cevher, V., Sankaranarayanan, A., Duarte, M.F., Reddy, D., Baraniuk, R.G., and Chellappa, R. (2008). Compressive sensing for background subtraction. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-540-88688-4_12"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TIP.2004.840704","article-title":"Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities","volume":"14","author":"Fadili","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/83.982822","article-title":"Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance","volume":"11","author":"Do","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1109\/JPROC.2010.2045630","article-title":"Precise Undersampling Theorems","volume":"98","author":"Donoho","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1137\/080714488","article-title":"Probing the Pareto frontier for basis pursuit solutions","volume":"31","author":"Berg","year":"2008","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0165-1684(98)00124-8","article-title":"Perceptual quality metrics applied to still image compression","volume":"70","author":"Eckert","year":"1998","journal-title":"Signal Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/8\/1002\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:38:09Z","timestamp":1760164689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/8\/1002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,31]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["e23081002"],"URL":"https:\/\/doi.org\/10.3390\/e23081002","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2021,7,31]]}}}