{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:26:42Z","timestamp":1781886402907,"version":"3.54.5"},"reference-count":64,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics.<\/jats:p>","DOI":"10.3390\/info13120588","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T07:59:21Z","timestamp":1671436761000},"page":"588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hybrid No-Reference Quality Assessment for Surveillance Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhongchang","family":"Ye","sequence":"first","affiliation":[{"name":"School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghua","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gonzalez-Cepeda, J., Ramajo, A., and Armingol, J.M. (2022). Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture. Information, 13.","DOI":"10.3390\/info13070325"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0212-5","article-title":"Intelligent video surveillance: A review through deep learning techniques for crowd analysis","volume":"6","author":"Sreenu","year":"2019","journal-title":"J. Big Data"},{"key":"ref_3","unstructured":"Muller-Schneiders, S., Jager, T., Loos, H.S., and Niem, W. (2005, January 15\u201316). Performance evaluation of a real time video surveillance system. Proceedings of the 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, China."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aqqa, M., Mantini, P., and Shah, S.K. (2019, January 25\u201327). Understanding How Video Quality Affects Object Detection Algorithms. Proceedings of the VISIGRAPP (5: VISAPP), Prague, Czech Republic.","DOI":"10.5220\/0007401600002108"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MC.2012.97","article-title":"Intelligent video surveillance","volume":"45","author":"Held","year":"2012","journal-title":"Computer"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Leszczuk, M., Romaniak, P., and Janowski, L. (2012). Quality assessment in video surveillance. Recent Developments in Video Surveillance, IntechOpen.","DOI":"10.5772\/30368"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhu, W., Zhai, G., Yao, C., and Yang, X. (2018, January 9\u201312). SIQD: Surveillance Image Quality Database and Performance Evaluation for Objective Algorithms. Proceedings of the 2018 IEEE Visual Communications and Image Processing (VCIP), Taichung, Taiwan.","DOI":"10.1109\/VCIP.2018.8698737"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Beghdadi, A., Qureshi, M.A., Dakkar, B.E., Gillani, H.H., Khan, Z.A., Kaaniche, M., Ullah, M., and Cheikh, F.A. (2022, January 16\u201319). A New Video Quality Assessment Dataset for Video Surveillance Applications. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897415"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bezzine, I., Khan, Z.A., Beghdadi, A., Al-Maadeed, N., Kaaniche, M., Al-Maadeed, S., Bouridane, A., and Cheikh, F.A. (2020, January 5\u20137). Video Quality Assessment Dataset for Smart Public Security Systems. Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.","DOI":"10.1109\/INMIC50486.2020.9318149"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, W., Sun, W., Min, X., Wang, T., and Zhai, G. (2022, January 16\u201319). Surveillance Video Quality Assessment Based on Quality Related Retraining. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897249"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11432-019-2757-1","article-title":"Perceptual image quality assessment: A survey","volume":"63","author":"Zhai","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457905","article-title":"Perceptual quality assessment of low-light image enhancement","volume":"17","author":"Zhai","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_13","unstructured":"Mohammadi, P., Ebrahimi-Moghadam, A., and Shirani, S. (2014). Subjective and objective quality assessment of image: A survey. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Golestaneh, S.A., Dadsetan, S., and Kitani, K.M. (2022, January 3\u20138). No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00404"},{"key":"ref_15","unstructured":"Sheikh, H. (2022, October 20). LIVE Image Quality Assessment Database Release 2. Available online: http:\/\/live.ece.utexas.edu\/research\/quality."},{"key":"ref_16","first-page":"30","article-title":"TID2008-a database for evaluation of full-reference visual quality assessment metrics","volume":"10","author":"Ponomarenko","year":"2009","journal-title":"Adv. Mod. Radioelectron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.image.2014.10.009","article-title":"Image database TID2013: Peculiarities, results and perspectives","volume":"30","author":"Ponomarenko","year":"2015","journal-title":"Signal Process. Image Commun."},{"key":"ref_18","unstructured":"Larson, E.C.D. (2022, October 20). Consumer Subjective Image Quality Database. Available online: http:\/\/visionokstateedu\/csiq\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/MSP.2011.942295","article-title":"Applications of objective image quality assessment methods [applications corner]","volume":"28","author":"Wang","year":"2011","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_20","unstructured":"Wang, L. (2021). A survey on IQA. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/659024","article-title":"Quality assessment of stereoscopic images","volume":"2008","author":"Benoit","year":"2009","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, H., Fang, Y., Lin, W., and Wang, Z. (2014, January 18\u201320). Subjective quality assessment of screen content images. Proceedings of the 2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX), Singapore.","DOI":"10.1109\/QoMEX.2014.6982328"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3345","DOI":"10.1109\/TIP.2015.2442920","article-title":"Perceptual quality assessment for multi-exposure image fusion","volume":"24","author":"Ma","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, J., and Lee, S. (2017, January 17\u201320). Deep blind image quality assessment by employing FR-IQA. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296869"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1109\/TIP.2012.2190086","article-title":"No-reference image quality assessment using visual codebooks","volume":"21","author":"Ye","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/LSP.2010.2045550","article-title":"A DCT statistics-based blind image quality index","volume":"17","author":"Saad","year":"2010","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3339","DOI":"10.1109\/TIP.2012.2191563","article-title":"Blind image quality assessment: A natural scene statistics approach in the DCT domain","volume":"21","author":"Saad","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1109\/TIP.2011.2147325","article-title":"Blind image quality assessment: From natural scene statistics to perceptual quality","volume":"20","author":"Moorthy","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4850","DOI":"10.1109\/TIP.2014.2355716","article-title":"Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features","volume":"23","author":"Xue","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lu, W., Sun, W., Zhu, W., Min, X., Zhang, Z., Wang, T., and Zhai, G. (2022). A cnn-based quality assessment method for pseudo 4k contents. Proceedings of the International Forum on Digital TV and Wireless Multimedia Communications, Springer.","DOI":"10.1007\/978-981-19-2266-4_13"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, T., Sun, W., Min, X., Lu, W., Zhang, Z., and Zhai, G. (2021, January 5\u20138). A Multi-dimensional Aesthetic Quality Assessment Model for Mobile Game Images. Proceedings of the 2021 International Conference on Visual Communications and Image Processing (VCIP), Munich, Germany.","DOI":"10.1109\/VCIP53242.2021.9675430"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/JSTSP.2019.2955024","article-title":"MC360IQA: A multi-channel CNN for blind 360-degree image quality assessment","volume":"14","author":"Sun","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_34","unstructured":"Ye, P., Kumar, J., Kang, L., and Doermann, D. (2012, January 16\u201321). Unsupervised feature learning framework for no-reference image quality assessment. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4444","DOI":"10.1109\/TIP.2016.2585880","article-title":"Blind image quality assessment based on high order statistics aggregation","volume":"25","author":"Xu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TCSVT.2018.2886771","article-title":"Blind image quality assessment using a deep bilinear convolutional neural network","volume":"30","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., and Zhang, Y. (2020, January 13\u201319). Blindly assess image quality in the wild guided by a self-adaptive hyper network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00372"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3650","DOI":"10.1109\/TIP.2021.3064195","article-title":"Blind image quality assessment with active inference","volume":"30","author":"Ma","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gao, X., Lu, W., Tao, D., and Li, X. (2010, January 11\u201314). Image quality assessment and human visual system. Proceedings of the Visual Communications and Image Processing, Huangshan, China.","DOI":"10.1117\/12.862431"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.dsp.2019.02.017","article-title":"Free-energy principle inspired visual quality assessment: An overview","volume":"91","author":"Zhai","year":"2019","journal-title":"Digit. Signal Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TIP.2011.2161092","article-title":"A psychovisual quality metric in free-energy principle","volume":"21","author":"Zhai","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TMM.2014.2373812","article-title":"Using free energy principle for blind image quality assessment","volume":"17","author":"Gu","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/TIP.2016.2639451","article-title":"Image quality assessment based on local linear information and distortion-specific compensation","volume":"26","author":"Wang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","unstructured":"Jakhetiya, V., Mumtaz, D., and Jaiswal, S.P. (2020, January 21\u201324). Distortion specific contrast based no-reference quality assessment of DIBR-synthesized views. Proceedings of the 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3468872","article-title":"Precise no-reference image quality evaluation based on distortion identification","volume":"17","author":"Yan","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.image.2011.08.002","article-title":"A new image quality assessment method to detect and measure strength of blocking artifacts","volume":"27","author":"Lee","year":"2012","journal-title":"Signal Process. Image Commun."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1109\/TIP.2005.854492","article-title":"No-reference quality assessment using natural scene statistics: JPEG2000","volume":"14","author":"Sheikh","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2678","DOI":"10.1109\/TIP.2011.2131660","article-title":"A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)","volume":"20","author":"Narvekar","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Min, X., Zhou, Q., He, J., Wang, Q., and Zhai, G. (2022). MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment. arXiv.","DOI":"10.24963\/ijcai.2023\/195"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF CVPR, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Min, X., Zhu, W., Wang, T., Lu, W., and Zhai, G. (2021, January 5\u20139). A no-reference evaluation metric for low-light image enhancement. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428312"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chowdhury, D., Das, S.K., Nandy, S., Chakraborty, A., Goswami, R., and Chakraborty, A. (2019, January 18\u201320). An Atomic Technique for Removal of Gaussian Noise from a Noisy Gray Scale Image Using LowPass-Convoluted Gaussian Filter. Proceedings of the International Conference on Opto-Electronics and Applied Optics, Kolkata, India.","DOI":"10.1109\/OPTRONIX.2019.8862330"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chang, C., Hsiao, J., and Hsieh, C. (2008, January 20\u201322). An Adaptive Median Filter for Image Denoising. Proceedings of the International Symposium on Intelligent Information Technology Application, Shanghai, China.","DOI":"10.1109\/IITA.2008.259"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Min, X., Wang, T., Lu, W., and Zhai, G. (2021, January 5\u20138). A Full-Reference Quality Assessment Metric for Fine-Grained Compressed Images. Proceedings of the 2021 International Conference on Visual Communications and Image Processing (VCIP), Munich, Germany.","DOI":"10.1109\/VCIP53242.2021.9675389"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Wu, W., Chen, Y., Min, X., and Zhai, G. (2022). Perceptual Quality Assessment for Fine-Grained Compressed Images. arXiv.","DOI":"10.1109\/VCIP53242.2021.9675389"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Min, X., Zhu, W., Wang, T., Lu, W., and Zhai, G. (2022). A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps. arXiv.","DOI":"10.1109\/ISCAS48785.2022.9937738"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1109\/TIP.2005.863120","article-title":"Semi-blind image restoration via Mumford-Shah regularization","volume":"15","author":"Bar","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dodge, S., and Karam, L. (2016, January 6\u20138). Understanding how image quality affects deep neural networks. Proceedings of the IEEE QoMEX, Lisbon, Portugal.","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, D., Jiang, T., and Jiang, M. (2019, January 21\u201325). Quality assessment of in-the-wild videos. Proceedings of the ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3351028"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sun, W., Min, X., Lu, W., and Zhai, G. (2022). A Deep Learning based No-reference Quality Assessment Model for UGC Videos. arXiv.","DOI":"10.1145\/3503161.3548329"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE\/CVF CVPR, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_62","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference for Learning Representations, Diego, CA, USA."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1109\/TMM.2018.2875354","article-title":"Which has better visual quality: The clear blue sky or a blurry animal?","volume":"21","author":"Li","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","article-title":"A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/12\/588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:05Z","timestamp":1760146985000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/12\/588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":64,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["info13120588"],"URL":"https:\/\/doi.org\/10.3390\/info13120588","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}