{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T07:02:52Z","timestamp":1768114972592,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2024 University Visiting Scholar \u201cTeacher Professional Development Project\u201d of Zhejiang Province in China","award":["FX2024129"],"award-info":[{"award-number":["FX2024129"]}]},{"name":"2024 University Visiting Scholar \u201cTeacher Professional Development Project\u201d of Zhejiang Province in China","award":["NZ21RC005"],"award-info":[{"award-number":["NZ21RC005"]}]},{"name":"2024 University Visiting Scholar \u201cTeacher Professional Development Project\u201d of Zhejiang Province in China","award":["62171243"],"award-info":[{"award-number":["62171243"]}]},{"name":"2024 University Visiting Scholar \u201cTeacher Professional Development Project\u201d of Zhejiang Province in China","award":["Y202044368"],"award-info":[{"award-number":["Y202044368"]}]},{"name":"2021 Talent Introduction and Cultivation Special Project of Ningbo Polytechnic","award":["FX2024129"],"award-info":[{"award-number":["FX2024129"]}]},{"name":"2021 Talent Introduction and Cultivation Special Project of Ningbo Polytechnic","award":["NZ21RC005"],"award-info":[{"award-number":["NZ21RC005"]}]},{"name":"2021 Talent Introduction and Cultivation Special Project of Ningbo Polytechnic","award":["62171243"],"award-info":[{"award-number":["62171243"]}]},{"name":"2021 Talent Introduction and Cultivation Special Project of Ningbo Polytechnic","award":["Y202044368"],"award-info":[{"award-number":["Y202044368"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["FX2024129"],"award-info":[{"award-number":["FX2024129"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NZ21RC005"],"award-info":[{"award-number":["NZ21RC005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171243"],"award-info":[{"award-number":["62171243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Y202044368"],"award-info":[{"award-number":["Y202044368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Fund of Zhejiang Provincial Education Department","award":["FX2024129"],"award-info":[{"award-number":["FX2024129"]}]},{"name":"Scientific Research Fund of Zhejiang Provincial Education Department","award":["NZ21RC005"],"award-info":[{"award-number":["NZ21RC005"]}]},{"name":"Scientific Research Fund of Zhejiang Provincial Education Department","award":["62171243"],"award-info":[{"award-number":["62171243"]}]},{"name":"Scientific Research Fund of Zhejiang Provincial Education Department","award":["Y202044368"],"award-info":[{"award-number":["Y202044368"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In marine remote sensing, underwater images play an indispensable role in ocean exploration, owing to their richness in information and intuitiveness. However, underwater images often encounter issues such as color shifts, loss of detail, and reduced clarity, leading to the decline of image quality. Therefore, it is critical to study precise and efficient methods for assessing underwater image quality. A no-reference multi-space feature fusion and entropy-based metrics for underwater image quality assessment (MFEM-UIQA) are proposed in this paper. Considering the color shifts of underwater images, the chrominance difference map is created from the chrominance space and statistical features are extracted. Moreover, considering the information representation capability of entropy, entropy-based multi-channel mutual information features are extracted to further characterize chrominance features. For the luminance space features, contrast features from luminance images based on gamma correction and luminance uniformity features are extracted. In addition, logarithmic Gabor filtering is applied to the luminance space images for subband decomposition and entropy-based mutual information of subbands is captured. Furthermore, underwater image noise features, multi-channel dispersion information, and visibility features are extracted to jointly represent the perceptual features. The experiments demonstrate that the proposed MFEM-UIQA surpasses the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/e27020173","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T06:30:29Z","timestamp":1738823429000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Space Feature Fusion and Entropy-Based Metrics for Underwater Image Quality Assessment"],"prefix":"10.3390","volume":"27","author":[{"given":"Baozhen","family":"Du","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Ningbo Polytechnic, Ningbo 315800, China"}]},{"given":"Hongwei","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China"}]},{"given":"Jiahao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China"}]},{"given":"Qunxin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Ningbo Polytechnic, Ningbo 315800, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hao, Y., Yuan, Y., Zhang, H., and Zhang, Z. (2024). Underwater Optical Imaging: Methods, Applications and Perspectives. Remote Sens., 16.","DOI":"10.3390\/rs16203773"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5644417","DOI":"10.1109\/TGRS.2024.3473020","article-title":"Neuromorphic Computing Network for Underwater Image Enhancement and Beyond","volume":"62","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"7501305","article-title":"Exploiting Deep Matching and Underwater Terrain Images to Improve Underwater Localization Accuracy","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lin, Z., He, Z., Jin, C., Luo, T., and Chen, Y. (2024). Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment. Remote Sens., 16.","DOI":"10.3390\/rs16163021"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shi, J., Li, H., Zhong, C., He, Z., and Ma, Y. (2022). BMEFIQA: Blind Quality Assessment of Multi-Exposure Fused Images Based on Several Characteristics. Entropy, 24.","DOI":"10.3390\/e24020285"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cui, Y. (2020). No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion. Entropy, 22.","DOI":"10.3390\/e22030344"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JOE.2023.3329202","article-title":"SISC: A Feature Interaction-Based Metric for Underwater Image Quality Assessment","volume":"49","author":"Chu","year":"2024","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201cCompletely Blind\u201d Image Quality Analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1109\/TIP.2015.2426416","article-title":"A Feature-Enriched Completely Blind Image Quality Evaluator","volume":"24","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TMM.2017.2788206","article-title":"Blind Quality Assessment Based on Pseudo-Reference Image","volume":"20","author":"Min","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_12","first-page":"100","article-title":"3An Effective General-Purpose NR-IQA Model Using Natural Scene Statistics (NSS) of the Luminance Relative Order. Signal Process","volume":"71","author":"Wang","year":"2019","journal-title":"Image Commun."},{"key":"ref_13","first-page":"101039","article-title":"An Efficient Approach for No-Reference Image Quality Assessment Based on Statistical Texture and Structural Features","volume":"30","author":"Rajevenceltha","year":"2022","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chu, Y., Chen, F., Fu, H., and Yu, H. (2022). Haze Level Evaluation Using Dark and Bright Channel Prior Information. Atmosphere, 13.","DOI":"10.2139\/ssrn.4004967"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3934","DOI":"10.1109\/TMM.2022.3168438","article-title":"Visibility and Distortion Measurement for No-Reference Dehazed Image Quality Assessment via Complex Contourlet Transform","volume":"25","author":"Guan","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8046","DOI":"10.1109\/TII.2021.3065439","article-title":"Perceptual Quality Evaluation of Hazy Natural Images","volume":"17","author":"Mahajan","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/LGRS.2016.2614890","article-title":"No-Reference Assessment on Haze for Remote-Sensing Images","volume":"13","author":"Pan","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3843","DOI":"10.1109\/TIV.2024.3356055","article-title":"Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception","volume":"9","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6062","DOI":"10.1109\/TIP.2015.2491020","article-title":"An Underwater Color Image Quality Evaluation Metric","volume":"24","author":"Yang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/JOE.2015.2469915","article-title":"Human-Visual-System-Inspired Underwater Image Quality Measures","volume":"41","author":"Panetta","year":"2016","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.compeleceng.2017.12.006","article-title":"An Imaging-Inspired No-Reference Underwater Color Image Quality Assessment Metric","volume":"70","author":"Wang","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116218","DOI":"10.1016\/j.image.2021.116218","article-title":"A Reference-Free Underwater Image Quality Assessment Metric in Frequency Domain","volume":"94","author":"Yang","year":"2021","journal-title":"Signal Process. Image Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5959","DOI":"10.1109\/TCSVT.2022.3164918","article-title":"Underwater Image Enhancement Quality Evaluation: Benchmark Dataset and Objective Metric","volume":"32","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","first-page":"71","article-title":"Underwater Image Quality Assessment from Synthetic to Real-World: Dataset and Objective Method","volume":"20","author":"Li","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102586","DOI":"10.1016\/j.displa.2023.102586","article-title":"No-Reference Quality Assessment of Underwater Image Enhancement","volume":"81","author":"Yi","year":"2024","journal-title":"Displays"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1109\/TMM.2023.3301226","article-title":"UIQI: A Comprehensive Quality Evaluation Index for Underwater Images","volume":"26","author":"Liu","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109293","DOI":"10.1016\/j.compeleceng.2024.109293","article-title":"No-Reference Quality Assessment for Underwater Images","volume":"118","author":"Hou","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103979","DOI":"10.1016\/j.jvcir.2023.103979","article-title":"A No-Reference Underwater Image Quality Evaluator via Quality-Aware Features","volume":"97","author":"Zhang","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Yi, X., Ouyang, L., Zhou, J., and Wang, Z. (2024). Towards Dimension-Enriched Underwater Image Quality Assessment. IEEE Trans. Circuits Syst. Video Technol. Early Access.","DOI":"10.1109\/TCSVT.2024.3466925"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7734","DOI":"10.1109\/TMM.2024.3371218","article-title":"Underwater Image Quality Assessment: Benchmark Database and Objective Method","volume":"26","author":"Liu","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/TPAMI.2006.3","article-title":"Boosting Color Saliency in Image Feature Detection","volume":"28","author":"Gevers","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","first-page":"4211615","article-title":"Underwater Image Restoration via Constrained Color Compensation and Background Light Color Space-Based Haze-Line Model","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zafar, S., Nizami, I.F., Rehman, M.U., Majid, M., and Ryu, J. (2023). NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram. Sensors, 23.","DOI":"10.3390\/s23125652"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5612","DOI":"10.1109\/TIP.2020.2984879","article-title":"No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics","volume":"29","author":"Channappayya","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14123","DOI":"10.1007\/s00521-021-06483-9","article-title":"A Bayesian Sampling Framework for Asymmetric Generalized Gaussian Mixture Models Learning","volume":"34","author":"Vemuri","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6698","DOI":"10.1109\/JSEN.2020.3043586","article-title":"Image Segmentation and Region Classification in Automotive High-Resolution Radar Imagery","volume":"21","author":"Xiao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s13640-019-0479-7","article-title":"No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality","volume":"2019","author":"Chen","year":"2019","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TBC.2023.3312932","article-title":"Integrates Spatiotemporal Visual Stimuli for Video Quality Assessment","volume":"70","author":"Guo","year":"2024","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Matkovi\u0107, K., Neumann, L., Neumann, A., Psik, T., and Purgathofer, W. (2005, January 18\u201320). Global Contrast Factor\u2014A New Approach to Image Contrast. Proceedings of the Computational Aesthetics 2005: Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging 2005, Girona, Spain.","DOI":"10.1111\/j.1467-8659.2006.00928.x"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Varga, D. (2020). No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features. J. Imaging, 6.","DOI":"10.3390\/jimaging6080075"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1109\/JSTARS.2022.3229392","article-title":"Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images","volume":"16","author":"Hong","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109847","DOI":"10.1016\/j.ymssp.2022.109847","article-title":"Phase-Based Motion Estimation in Complex Environments Using the Illumination- Invariant Log-Gabor Filter","volume":"186","author":"Wang","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1109\/TBC.2023.3291139","article-title":"Human Perception-Oriented Enhancement and Smoothing for Perceptual Video Coding","volume":"69","author":"Kang","year":"2023","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/LGRS.2019.2960095","article-title":"Fast Shape Parameter Estimation of the Complex Generalized Gaussian Distribution in SAR Images","volume":"17","author":"Leng","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108384","DOI":"10.1109\/ACCESS.2019.2932018","article-title":"Drifted Twitter Spam Classification Using Multiscale Detection Test on K-L Divergence","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TCSVT.2022.3208100","article-title":"A Perception-Aware Decomposition and Fusion Framework for Underwater Image Enhancement","volume":"33","author":"Kang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12611","DOI":"10.1109\/ACCESS.2021.3050747","article-title":"Matching Intensity for Image Visibility Graphs: A New Method to Extract Image Features","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1109\/LGRS.2015.2453636","article-title":"Estimation of Water Depths and Turbidity From Hyperspectral Imagery Using Support Vector Regression","volume":"12","author":"Pan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chavhan, Y.D., Yelure, B.S., and Tayade, K.N. (2015, January 26\u201327). Speech Emotion Recognition Using RBF Kernel of LIBSVM. Proceedings of the 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.","DOI":"10.1109\/ECS.2015.7124760"},{"key":"ref_50","first-page":"151","article-title":"UID2021: An Underwater Image Dataset for Evaluation of No-Reference Quality Assessment Metrics. ACM Trans","volume":"19","author":"Hou","year":"2023","journal-title":"Multimed. Comput. Commun. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1109\/TIP.2015.2456502","article-title":"Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging","volume":"24","author":"Choi","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","unstructured":"Jaiantilal, A. (2018, June 16). Random Forest Implementation for MATLAB. Available online: https:\/\/code.google.com\/archive\/p\/randomforest-matlab\/."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/2\/173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:28:02Z","timestamp":1760027282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/2\/173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,6]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["e27020173"],"URL":"https:\/\/doi.org\/10.3390\/e27020173","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,6]]}}}