{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:23:42Z","timestamp":1760145822751,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research on Cloud Edge Collaborative Intelligent Image Analysis Technology","award":["J2023003"],"award-info":[{"award-number":["J2023003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video compressive sensing algorithm for transmission line surveillance video compression. The proposed method compresses foreground targets and background frames separately and uses reversible neural network to reconstruct original frames. The result on transmission line surveillance video data shows that the proposed compressive sensing method can achieve 26.47, 34.71 PSNR and 0.6839, 0.9320 SSIM higher than existing methods on 1.5% and 15% measurement rates, and the proposed ROI extraction net can precisely retrieve regions under high noise levels. This research not only demonstrates the potential for a more efficient video compression technique in resource-constrained environments, but also lays a foundation for future advancements in video compressive sensing techniques and their applications in various real-time surveillance systems.<\/jats:p>","DOI":"10.3390\/info15090555","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T03:16:51Z","timestamp":1725938211000},"page":"555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Mixed-Rate Region-of-Interest-Aware Video Compressive Sensing for Transmission Line Surveillance Video"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9706-3654","authenticated-orcid":false,"given":"Lisha","family":"Gao","sequence":"first","affiliation":[{"name":"Nanjing Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhoujun","family":"Ma","sequence":"additional","affiliation":[{"name":"Nanjing Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Han","sequence":"additional","affiliation":[{"name":"Nanjing Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiancheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nanjing Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingcheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangjie","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1002\/cpa.20124","article-title":"Stable Signal Recovery from Incomplete and Inaccurate Measurements","volume":"59","author":"Romberg","year":"2006","journal-title":"Comm. Pure Appl. Math."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102591","DOI":"10.1016\/j.dsp.2019.102591","article-title":"DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing","volume":"96","author":"Iliadis","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_3","first-page":"5687","article-title":"Compressed Learning: A Deep Neural Network Approach","volume":"65","author":"Adler","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.dsp.2017.09.010","article-title":"Deep Fully-Connected Networks for Video Compressive Sensing","volume":"72","author":"Iliadis","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Masci, J., Meier, U., Ciresan, D., and Schmidhuber, J. (2011, January 14\u201317). Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. Proceedings of the International Conference on Artificial Neural Networks, Espoo, Finland.","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"ref_6","unstructured":"Bora, A., Jalal, A., Price, E., and Dimakis, A.G. (2017, January 6\u201311). Compressed Sensing Using Generative Models. Proceedings of the 34th International Conference on Machine Learning-Volume 70, Sydney, Australia."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","unstructured":"Liu, D., Wen, B., Fan, Y., Loy, C.C., and Huang, T.S. (2018, January 2\u20138). Non-Local Recurrent Network for Image Restoration. Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montr\u00e9al, ON, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yuan, X. (2016, January 25\u201328). Generalized Alternating Projection Based Total Variation Minimization for Compressive Sensing. Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532817"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yuan, X., Liu, Y., Suo, J., and Dai, Q. (2020, January 13\u201319). Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00152"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Lu, R., Wang, Z., Zhang, H., Chen, B., Meng, Z., and Yuan, X. (2020, January 23\u201328). BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58586-0_16"},{"key":"ref_13","unstructured":"Z, W., Zhang, J., and Mou, C. (2021, January 10\u201317). Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, L., Cao, M., Zhong, Y., and Yuan, X. (2022). Spatial-Temporal Transformer for Video Snapshot Compressive Imaging. arXiv.","DOI":"10.1109\/TPAMI.2022.3225382"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TCSVT.2016.2527181","article-title":"Video Compressive Sensing Reconstruction via Reweighted Residual Sparsity","volume":"27","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., and Ashok, A. (2016, January 27\u201330). ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.55"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xie, X., Wang, Y., Shi, G., Wang, C., Du, J., and Han, X. (2017, January 11\u201314). Adaptive Measurement Network for CS Image Reconstruction. Proceedings of the Computer Vision: Second CCF Chinese Conference, CCCV 2017, Tianjin, China. Proceedings, Part II.","DOI":"10.1007\/978-981-10-7302-1_34"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, K., and Ren, F. (2018, January 12\u201315). CSVideoNet: A Real-Time End-to-End Learning Framework for High-Frame-Rate Video Compressive Sensing. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00187"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, F., Jiang, H., Shen, Z., Deng, W., and Metaxas, D. (2013, January 15\u201318). Adaptive Low Rank and Sparse Decomposition of Video Using Compressive Sensing. Proceedings of the IEEE International Conference on Image Processing, Melbourne, VIC, Australia.","DOI":"10.1109\/ICIP.2013.6738210"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1109\/TCSVT.2020.3027741","article-title":"Foreground-Background Parallel Compression with Residual Encoding for Surveillance Video","volume":"31","author":"Wu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Du, J., Xie, X., and Shi, G. (2021, January 22\u201325). Multi-Rate Video Compressive Sensing for Fixed Scene Measurement. Proceedings of the The 5th International Conference on Video and Image Processing, Hayward, CA, USA.","DOI":"10.1145\/3511176.3511203"},{"key":"ref_22","unstructured":"Gomez, A.N., Ren, M., Urtasun, R., and Grosse, R.B. (2017, January 4\u20139). The Reversible Residual Network: Backpropagation Without Storing Activations. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_23","unstructured":"Jacobsen, J.H., Smeulders, A.W., and Oyallon, E. (2018). i-revnet: Deep Invertible Networks. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., and Ishwar, P. (2012, January 16\u201321). Changedetection.net: A New Change Detection Benchmark Dataset. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238919"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Elgammal, A., Harwood, D., and Davis, L. (July, January 26). Non-Parametric Model for Background Subtraction. Proceedings of the Computer Vision\u2014ECCV 2000: 6th European Conference on Computer Vision, Dublin, Ireland. Proceedings, Part II.","DOI":"10.1007\/3-540-45053-X_48"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/555\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:52:33Z","timestamp":1760111553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["info15090555"],"URL":"https:\/\/doi.org\/10.3390\/info15090555","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,9,10]]}}}