{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T05:33:59Z","timestamp":1781847239745,"version":"3.54.5"},"reference-count":88,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.<\/jats:p>","DOI":"10.3390\/jimaging10100243","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T09:16:10Z","timestamp":1727428570000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Overview of High-Dynamic-Range Image Quality Assessment"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3196-6866","authenticated-orcid":false,"given":"Yue","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8147-8334","authenticated-orcid":false,"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3583-959X","authenticated-orcid":false,"given":"Shiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7517-3868","authenticated-orcid":false,"given":"Xinfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Chinese Academic of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7484-7261","authenticated-orcid":false,"given":"Sam","family":"Kwong","sequence":"additional","affiliation":[{"name":"School of Data Sciences, Lingnan University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Metzler, C.A., Ikoma, H., Peng, Y., and Wetzstein, G. (2020, January 13\u201319). Deep optics for single-shot high-dynamic-range imaging. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00145"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shopovska, I., Stojkovic, A., Aelterman, J., Van Hamme, D., and Philips, W. (2023). High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems. Sensors, 23.","DOI":"10.3390\/s23125767"},{"key":"ref_3","first-page":"51","article-title":"HRFlexToT: A high dynamic range ASIC for time-of-flight positron emission tomography","volume":"6","author":"Mauricio","year":"2021","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107805","DOI":"10.1016\/j.compag.2023.107805","article-title":"Extracting vegetation information from high dynamic range images with shadows: A comparison between deep learning and threshold methods","volume":"208","author":"Wang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102008","DOI":"10.1117\/1.OE.52.10.102008","article-title":"Tone mapping-based high-dynamic-range image compression: Study of optimization criterion and perceptual quality","volume":"52","author":"Narwaria","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Korshunov, P., Hanhart, P., Richter, T., Artusi, A., Mantiuk, R., and Ebrahimi, T. (2015, January 26\u201329). Subjective quality assessment database of HDR images compressed with JPEG XT. Proceedings of the 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), Costa Navarino, Greece.","DOI":"10.1109\/QoMEX.2015.7148119"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Valenzise, G., De Simone, F., Lauga, P., and Dufaux, F. (2014, January 18\u201321). Performance evaluation of objective quality metrics for HDR image compression. Proceedings of the Applications of Digital Image Processing XXXVII, San Diego, CA, USA.","DOI":"10.1117\/12.2063032"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mantiuk, R., Daly, S.J., Myszkowski, K., and Seidel, H.P. (2005, January 17\u201320). Predicting visible differences in high dynamic range images: Model and its calibration. Proceedings of the Human Vision and Electronic Imaging X, San Jose, CA, USA.","DOI":"10.1117\/12.586757"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/2010324.1964935","article-title":"HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions","volume":"30","author":"Mantiuk","year":"2011","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_10","unstructured":"Mantiuk, R.K., Hammou, D., and Hanji, P. (2023). HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.image.2015.04.009","article-title":"HDR-VQM: An objective quality measure for high dynamic range video","volume":"35","author":"Narwaria","year":"2015","journal-title":"Signal Process. Image Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1109\/TCSVT.2023.3237702","article-title":"High dynamic range image quality assessment based on frequency disparity","volume":"33","author":"Liu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Choudhury, A., and Daly, S. (2018, January 26\u201329). HDR image quality assessment using machine-learning based combination of quality metrics. Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA.","DOI":"10.1109\/GlobalSIP.2018.8646579"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, P., Mantiuk, R.K., and Ma, K. (2024, January 16\u201322). Perceptual Assessment and Optimization of HDR Image Rendering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02117"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rousselot, M., Le Meur, O., Cozot, R., and Ducloux, X. (2019). Quality assessment of HDR\/WCG images using HDR uniform color spaces. J. Imaging, 5.","DOI":"10.3390\/jimaging5010018"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ward, G., and Simmons, M. (2006). JPEG-HDR: A backwards-compatible, high dynamic range extension to JPEG. ACM SIGGRAPH 2006 Courses, ACM.","DOI":"10.1145\/1185657.1185685"},{"key":"ref_17","unstructured":"Sugiyama, N., Kaida, H., Xue, X., Jinno, T., Adami, N., and Okuda, M. (2009, January 19\u201324). HDR image compression using optimized tone mapping model. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11554-015-0547-x","article-title":"Overview and evaluation of the JPEG XT HDR image compression standard","volume":"16","author":"Artusi","year":"2019","journal-title":"J. Real-Time Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TCSVT.2021.3101953","article-title":"Overview of the versatile video coding (VVC) standard and its applications","volume":"31","author":"Bross","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","first-page":"3","article-title":"Algorithm description for Versatile Video Coding and Test Model 16 (VTM 16)","volume":"16","author":"Browne","year":"2022","journal-title":"Jt. Video Expert. Team (JVET) ITu-T SG"},{"key":"ref_21","unstructured":"Narwaria, M., Da Silva, M.P., Le Callet, P., and P\u00e9pion, R. (2014, January 30\u201331). Impact of tone mapping in high dynamic range image compression. Proceedings of the VPQM, Chandler, AZ, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41233-017-0007-4","article-title":"An extensive performance evaluation of full-reference HDR image quality metrics","volume":"2","author":"Zerman","year":"2017","journal-title":"Qual. User Exp."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"52","DOI":"10.5594\/j18290","article-title":"Perceptual signal coding for more efficient usage of bit codes","volume":"122","author":"Miller","year":"2013","journal-title":"SMPTE Motion Imaging J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1109\/TMM.2021.3076298","article-title":"Consolidated dataset and metrics for high-dynamic-range image quality","volume":"24","author":"Mikhailiuk","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_25","first-page":"1139","article-title":"From pairwise comparisons and rating to a unified quality scale","volume":"29","author":"Mikhailiuk","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.1007\/s13042-024-02151-1","article-title":"HDRC: A subjective quality assessment database for compressed high dynamic range image","volume":"15","author":"Liu","year":"2024","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.jvcir.2007.06.003","article-title":"iCAM06: A refined image appearance model for HDR image rendering","volume":"18","author":"Kuang","year":"2007","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_29","unstructured":"ITU (2024, September 25). Video Quality Assessment Methods for Multimedia Applications. ITU-T Recommendation P.910, Available online: https:\/\/www.itu.int\/rec\/T-REC-P.910-202310-I\/en."},{"key":"ref_30","unstructured":"(2024, September 25). Available online: https:\/\/hdr.sim2.it\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3364","DOI":"10.1109\/TIP.2012.2197010","article-title":"Fourier transform-based scalable image quality measure","volume":"21","author":"Narwaria","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1145\/1166087.1166095","article-title":"A perceptual framework for contrast processing of high dynamic range images","volume":"3","author":"Mantiuk","year":"2006","journal-title":"ACM Trans. Appl. Percept. (TAP)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1145\/566654.566575","article-title":"Photographic tone reproduction for digital images","volume":"21","author":"Reinhard","year":"2002","journal-title":"ACM Trans. Graph."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Durand, F., and Dorsey, J. (2002, January 23\u201326). Fast bilateral filtering for the display of high-dynamic-range images. Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, San Antonio, TX, USA.","DOI":"10.1145\/566570.566574"},{"key":"ref_35","unstructured":"Ashikhmin, M. (2002, January 26\u201328). A tone mapping algorithm for high contrast images. Proceedings of the 13th Eurographics workshop on Rendering, Pisa, Italy."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Richter, T. (2013, January 8\u201311). On the standardization of the JPEG XT image compression. Proceedings of the 2013 Picture Coding Symposium (PCS), San Jose, CA, USA.","DOI":"10.1109\/PCS.2013.6737677"},{"key":"ref_37","unstructured":"ITU (2024, September 25). Methodology for the Subjective Assessment of the Quality of Television Pictures. ITU-R Recommendation BT.500. Available online: https:\/\/www.itu.int\/rec\/R-REC-BT.500-15-202305-I\/en."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1109\/TIP.2010.2095866","article-title":"Optimizing a Tone Curve for Backward-Compatible High Dynamic Range Image and Video Compression","volume":"20","author":"Mai","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ayd\u0131n, T.O., Mantiuk, R., and Seidel, H.P. (2008, January 28\u201331). Extending quality metrics to full luminance range images. Proceedings of the Human Vision and Electronic Imaging Xiii, San Jose, CA, USA.","DOI":"10.1117\/12.765095"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"010501","DOI":"10.1117\/1.JEI.24.1.010501","article-title":"HDR-VDP-2.2: A calibrated method for objective quality prediction of high-dynamic range and standard images","volume":"24","author":"Narwaria","year":"2015","journal-title":"J. Electron. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Thurstone, L.L. (2017). A law of comparative judgment. Scaling, Routledge.","DOI":"10.4324\/9781315128948-7"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1109\/TIP.2022.3144892","article-title":"VCRNet: Visual compensation restoration network for no-reference image quality assessment","volume":"31","author":"Pan","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","first-page":"2567","article-title":"Image quality assessment: Unifying structure and texture similarity","volume":"44","author":"Ding","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7589","DOI":"10.1109\/TCSVT.2024.3374887","article-title":"Causal Representation Learning for GAN-Generated Face Image Quality Assessment","volume":"34","author":"Tian","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ni, Z., Liu, Y., Ding, K., Yang, W., Wang, H., and Wang, S. (2024). Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics. IEEE Trans. Multimed., early access.","DOI":"10.1109\/TMM.2024.3405729"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1109\/TIP.2024.3393754","article-title":"Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment","volume":"33","author":"Chen","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8596","DOI":"10.1109\/TMM.2024.3380260","article-title":"Perceptual quality assessment of face video compression: A benchmark and an effective method","volume":"26","author":"Li","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_48","unstructured":"Zhu, H., Wu, H., Li, Y., Zhang, Z., Chen, B., Zhu, L., Fang, Y., Zhai, G., Lin, W., and Wang, S. (2024). Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.1109\/TMM.2023.3310268","article-title":"Towards Thousands to One Reference: Can We Trust the Reference Image for Quality Assessment?","volume":"26","author":"Tian","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TBC.2016.2623241","article-title":"Fast intra prediction based on content property analysis for low complexity HEVC-based screen content coding","volume":"63","author":"Lei","year":"2016","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.patrec.2007.11.012","article-title":"Consensus unsupervised feature ranking from multiple views","volume":"29","author":"Hong","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hanji, P., Mantiuk, R., Eilertsen, G., Hajisharif, S., and Unger, J. (2022, January 7\u201311). Comparison of single image HDR reconstruction methods\u2014the caveats of quality assessment. Proceedings of the ACM SIGGRAPH 2022 Conference Proceedings, Vancouver, BC, Canada.","DOI":"10.1145\/3528233.3530729"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Daly, S.J. (1992, January 10\u201313). Visible differences predictor: An algorithm for the assessment of image fidelity. Proceedings of the Human Vision, Visual Processing, and Digital Display III, San Jose, CA, USA.","DOI":"10.1117\/12.135952"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/j.1475-1313.1991.tb00200.x","article-title":"A simple parametric model of the human ocular modulation transfer function","volume":"11","author":"Deeley","year":"1991","journal-title":"Ophthalmic Physiol. Opt."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1016\/S0042-6989(00)00021-3","article-title":"The spectral sensitivities of the middle-and long-wavelength-sensitive cones derived from measurements in observers of known genotype","volume":"40","author":"Stockman","year":"2000","journal-title":"Vis. Res."},{"key":"ref_56","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."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360668","article-title":"Dynamic range independent image quality assessment","volume":"27","author":"Aydin","year":"2008","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0734-189X(87)80184-6","article-title":"The cortex transform- Rapid computation of simulated neural images","volume":"39","author":"Watson","year":"1987","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1109\/TIP.2017.2778570","article-title":"Blind quality estimation by disentangling perceptual and noisy features in high dynamic range images","volume":"27","author":"Kottayil","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","unstructured":"Pinson, M.H., and Wolf, S. (2003, January 8\u201311). An objective method for combining multiple subjective data sets. Proceedings of the Visual Communications and Image Processing 2003, Lugano, Switzerland."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1364\/JOSAA.4.002379","article-title":"Relations between the statistics of natural images and the response properties of cortical cells","volume":"4","author":"Field","year":"1987","journal-title":"JOSA A"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jia, S., Zhang, Y., Agrafiotis, D., and Bull, D. (2017, January 17\u201320). Blind high dynamic range image quality assessment using deep learning. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296384"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Prashnani, E., Cai, H., Mostofi, Y., and Sen, P. (2018, January 18\u201323). Pieapp: Perceptual image-error assessment through pairwise preference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00194"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mantiuk, R.K., and Azimi, M. (July, January 29). PU21: A novel perceptually uniform encoding for adapting existing quality metrics for HDR. Proceedings of the 2021 Picture Coding Symposium (PCS), Bristol, UK.","DOI":"10.1109\/PCS50896.2021.9477471"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mantiuk, R.K., Kim, M., Ashraf, M., Xu, Q., Luo, M.R., Martinovic, J., and Wuerger, S. (2020, January 4\u201319). Practical Color Contrast Sensitivity Functions for Luminance Levels up to 10000 cd\/m2. Proceedings of the Color and Imaging Conference. Society for Imaging Science & Technology, Online.","DOI":"10.2352\/issn.2169-2629.2020.28.1"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"50","DOI":"10.5594\/JMI.2016.2548838","article-title":"A display-independent high dynamic range television system","volume":"125","author":"Borer","year":"2016","journal-title":"SMPTE Motion Imaging J."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhang, Q., Feng, Y., Li, H., Wang, X., and Wang, Q. (2022, January 18\u201324). Hdr-nerf: High dynamic range neural radiance fields. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01785"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chen, X., Liu, Y., Zhang, Z., Qiao, Y., and Dong, C. (2021, January 20\u201325). Hdrunet: Single image hdr reconstruction with denoising and dequantization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00045"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5923","DOI":"10.1109\/TIP.2022.3203562","article-title":"Flexhdr: Modeling alignment and exposure uncertainties for flexible hdr imaging","volume":"31","author":"Tanay","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1037\/h0046162","article-title":"On the psychophysical law","volume":"64","author":"Stevens","year":"1957","journal-title":"Psychol. Rev."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/3386569.3392403","article-title":"Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss","volume":"39","author":"Santos","year":"2020","journal-title":"ACM Trans. Graph."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1109\/TIP.2022.3160070","article-title":"Attention-guided progressive neural texture fusion for high dynamic range image restoration","volume":"31","author":"Chen","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-015-0091-4","article-title":"Benchmarking of objective quality metrics for HDR image quality assessment","volume":"2015","author":"Hanhart","year":"2015","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","article-title":"Floating search methods in feature selection","volume":"15","author":"Pudil","year":"1994","journal-title":"Pattern Recognit. Lett."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1167\/jov.24.4.5","article-title":"castleCSF\u2014A contrast sensitivity function of color, area, spatiotemporal frequency, luminance and eccentricity","volume":"24","author":"Ashraf","year":"2024","journal-title":"J. Vis."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TIP.2013.2293423","article-title":"Gradient magnitude similarity deviation: A highly efficient perceptual image quality index","volume":"23","author":"Xue","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_77","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","article-title":"FSIM: A feature similarity index for image quality assessment","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TIP.2005.859389","article-title":"An information fidelity criterion for image quality assessment using natural scene statistics","volume":"14","author":"Sheikh","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4818","DOI":"10.1109\/TIP.2017.2718185","article-title":"ESIM: Edge similarity for screen content image quality assessment","volume":"26","author":"Ni","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.1109\/TIP.2018.2839890","article-title":"A Gabor feature-based quality assessment model for the screen content images","volume":"27","author":"Ni","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.aej.2024.02.007","article-title":"CN-BSRIQA: Cascaded network-blind super-resolution image quality assessment","volume":"91","author":"Rehman","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_83","unstructured":"Duffy, V.G. (2024). Empowering Zero-Shot Object Detection: A Human-in-the-Loop Strategy for Unveiling Unseen Realms in Visual Data. Proceedings of the Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, Springer."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1109\/TIP.2018.2874283","article-title":"Fine-grained quality assessment for compressed images","volume":"28","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_85","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_86","unstructured":"Alexey, D. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"109228","DOI":"10.1016\/j.patcog.2022.109228","article-title":"An effective CNN and Transformer complementary network for medical image segmentation","volume":"136","author":"Yuan","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, C., Deng, Y., Xie, B., Liu, T., and Li, Y.F. (2023). TransIFC: Invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Trans. Multimed., early access.","DOI":"10.1109\/TMM.2023.3238548"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/10\/243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:05:13Z","timestamp":1760112313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/10\/243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":88,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["jimaging10100243"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10100243","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]}}}