{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:21:05Z","timestamp":1778149265879,"version":"3.51.4"},"reference-count":84,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Institute for Information and Communications Technology Promotion (IITP) funded by the Korean Government","award":["2020-0-00347"],"award-info":[{"award-number":["2020-0-00347"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO\/IEC 10918). A novel \u201cFlicker Test Software\u201d is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.<\/jats:p>","DOI":"10.3390\/s23031297","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T04:50:41Z","timestamp":1674449441000},"page":"1297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9201-5028","authenticated-orcid":false,"given":"Afnan","family":"Afnan","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6175-889X","authenticated-orcid":false,"given":"Faiz","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9684-423X","authenticated-orcid":false,"given":"Yaseen","family":"Yaseen","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhee","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7139-7389","authenticated-orcid":false,"given":"Sonain","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9877-8982","authenticated-orcid":false,"given":"Oh-Jin","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E136","DOI":"10.1503\/cmaj.190434","article-title":"Smartphones, social media use and youth mental health","volume":"192","author":"Naylor","year":"2020","journal-title":"Can. Med. Assoc. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aljuaid, H., and Parah, S.A. (2021). Secure patient data transfer using information embedding and hyperchaos. Sensors, 21.","DOI":"10.3390\/s21010282"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"82511","DOI":"10.1109\/ACCESS.2022.3195891","article-title":"Image compression techniques in wireless sensor networks: A survey and comparison","volume":"10","author":"Lungisani","year":"2022","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Varga, D. (2022). No-reference video quality assessment using multi-pooled, saliency weighted deep features and decision fusion. Sensors., 22.","DOI":"10.3390\/s22062209"},{"key":"ref_5","unstructured":"Wakin, M., Romberg, J., Choi, H., and Baraniuk, R. (2002, January 22\u201325). Rate-distortion optimized image compression using wedge lets. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neucom.2019.12.015","article-title":"Reduction of JPEG compression artifacts based on DCT coefficients prediction","volume":"384","author":"Sun","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jenadeleh, M., Pedersen, M., and Saupe, D. (2020). Blind quality assessment of iris images acquired in visible light for biometric recognition. Sensors, 20.","DOI":"10.3390\/s20051308"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dumic, E., Bjelopera, A., and N\u00fcchter, A. (2021). Dynamic point cloud compression based on projections, surface reconstruction and video compression. Sensors, 22.","DOI":"10.3390\/s22010197"},{"key":"ref_9","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_10","first-page":"39","article-title":"The survey of subjective and objective methods for quality assessment of 2D and 3D images","volume":"26","author":"Opozda","year":"2014","journal-title":"Theor. Appl. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5859","DOI":"10.1109\/TCSVT.2022.3163860","article-title":"Large-scale crowdsourced subjective assessment of picture wise just noticeable difference","volume":"32","author":"Lin","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","unstructured":"ITU-R Recommendation, B.T. (2002). 500-11. Methodology for the Subjective Assessment of the Quality of Television Pictures, ITU."},{"key":"ref_13","first-page":"302","article-title":"Review of subjective quality assessment methodologies and standards for compressed images evaluation","volume":"11842","author":"Testolina","year":"2021","journal-title":"Applications of Digital Image Processing XLIV"},{"key":"ref_14","unstructured":"(2021). Information Technology\u2014Advanced Image Coding and Evaluation\u2014Part 2: Evaluation Procedure for Nearly Lossless Coding (Standard No. ISO\/IEC 29170-2:2015)."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wang, X., Li, B., Tian, M., and Yao, H. (2021). Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos. Sensors, 21.","DOI":"10.3390\/s21165322"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Hu, X., Gou, R., Zhang, L., Zheng, B., and Shen, Z. (2022). Rich Structural Index for Stereoscopic Image Quality Assessment. Sensors, 22.","DOI":"10.3390\/s22020499"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mahdaoui, A.E., Ouahabi, A., and Moulay, M.S. (2022). Image denoising using a compressive sensing approach based on regularization constraints. Sensors, 22.","DOI":"10.3390\/s22062199"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3352","DOI":"10.1109\/TCSVT.2020.3041639","article-title":"Data-Driven Transform-Based Compressed Image Quality Assessment","volume":"31","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technology."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Testolina, M., Upenik, E., Ascenso, J., Pereira, F., and Ebrahimi, T. (2021, January 14\u201317). Performance evaluation of objective image quality metrics on conventional and learning-based compression artifacts. Proceedings of the 13th International Conference on Quality of Multimedia Experience (QoMEX), Online.","DOI":"10.1109\/QoMEX51781.2021.9465445"},{"key":"ref_20","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_21","unstructured":"Li, X. (2002, January 22\u201325). Blind image quality assessment. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Varga, D. (2022). A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors. Sensors, 22.","DOI":"10.3390\/s22186775"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"St\u0119pie\u0144, I., and Oszust, M. (2022). A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. J. Imaging., 8.","DOI":"10.3390\/jimaging8060160"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1080\/02564602.2016.1151385","article-title":"No-reference\/blind image quality assessment: A survey","volume":"34","author":"Xu","year":"2017","journal-title":"IETE Tech. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1016\/j.ijleo.2015.02.093","article-title":"No-reference image quality assessment algorithms: A survey","volume":"126","author":"Kamble","year":"2015","journal-title":"Optik"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, W., Sun, W., Min, X., Zhu, W., Zhou, Q., He, J., Wang, Q., Zhang, Z., Wang, T., and Zhai, G. (2022). Deep Neural Network for Blind Visual Quality Assessment of 4K Content. arXiv.","DOI":"10.1109\/TBC.2022.3221689"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Golestaneh, S.A., Dadsetan, S., and Kitani, K. (2022, January 4\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_28","doi-asserted-by":"crossref","unstructured":"Lu, W., Sun, W., Min, X., Zhu, W., Zhou, Q., He, J., Wang, Q., Zhang, Z., Wang, T., and Zhai, G. (2022). No-reference panoramic image quality assessment based on multi-region adjacent pixels correlation. PloS One, 17.","DOI":"10.1371\/journal.pone.0266021"},{"key":"ref_29","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_30","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_31","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":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., and Zhang, Y. (2020, January 14\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, Online.","DOI":"10.1109\/CVPR42600.2020.00372"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/TCSVT.2021.3073410","article-title":"Generalizable no-reference image quality assessment via deep meta-learning","volume":"32","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.dsp.2018.05.010","article-title":"Blind image quality assessment in multiple bandpass and redundancy domains","volume":"80","author":"Ma","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, D., Jiang, T., and Jiang, M. (2020, January 12\u201316). Norm-in-norm loss with faster convergence and better performance for image quality assessment. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413804"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., and Bovik, A. (2020, January 13\u201319). From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00363"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhou, W., Xu, J., Li, X., An, S., and Chen, Z. (2021). LIQA: Lifelong Blind Image Quality Assessment. arXiv.","DOI":"10.1109\/TMM.2022.3190700"},{"key":"ref_38","first-page":"1","article-title":"Continual learning for blind image quality assessment","volume":"Early Access","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sun, S., Yu, T., Xu, J., Zhou, W., and Chen, Z. (2022). GraphIQA: Learning distortion graph representations for blind image quality assessment. IEEE Trans. Multimed., 1.","DOI":"10.1109\/TMM.2022.3152942"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Balanov, A., Schwartz, A., and Moshe, Y. (2016, January 6\u20138). Reduced-reference image quality assessment based on dct subband similarity. Proceedings of the 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), Lisbon, Portugal.","DOI":"10.1109\/QoMEX.2016.7498930"},{"key":"ref_41","unstructured":"Gu, K., Zhai, G., Yang, X., and Zhang, W. (2013, January 19\u201323). A new reduced-reference image quality assessment using structural degradation model. Proceedings of the 2013 IEEE international symposium on circuits and systems (ISCAS), Beijing, China."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gu, K., Zhai, G., Yang, X., Zhang, W., and Liu, M. (2013, January 15\u201318). Subjective and objective quality assessment for images with contrast change. Proceedings of the 2013 IEEE International Conference on Image Processing, Mlebourne, VI, Australia.","DOI":"10.1109\/ICIP.2013.6738079"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2016.02.043","article-title":"Orientation selectivity based visual pattern for reduced-reference image quality assessment","volume":"351","author":"Wu","year":"2016","journal-title":"Inf. Sci.."},{"key":"ref_44","unstructured":"Phadikar, B.S., Maity, G.K., and Phadikar, A. (2018). Industry Interactive Innovations in Science, Engineering and Technology, Springer."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"303","DOI":"10.15623\/ijret.2013.0212052","article-title":"A survey on full reference image quality assessment algorithms","volume":"2","author":"George","year":"2013","journal-title":"Int. J. Res. Eng. Technol."},{"key":"ref_46","unstructured":"Pedersen, M., and Hardeberg, J.Y. (2022, December 18). Survey of Full-reference Image QUALITY metrics. Available online: https:\/\/ntnuopen.ntnu.no\/ntnu-xmlui\/bitstream\/handle\/11250\/144194\/rapport052009_elektroniskversjon.pdf?sequence=1."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-02238-8","article-title":"Modern image quality assessment","volume":"2","author":"Wang","year":"2006","journal-title":"Synth. Lect. Image Video Multimed. Process."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/TBC.2014.2344471","article-title":"Hybrid no-reference quality metric for singly and multiply distorted images","volume":"60","author":"Gu","year":"2014","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/83.841940","article-title":"Image quality assessment based on a degradation model","volume":"9","author":"Kite","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TIP.2010.2092435","article-title":"Information content weighting for perceptual image quality assessment","volume":"20","author":"Wang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"011006","DOI":"10.1117\/1.3267105","article-title":"Most apparent distortion: Full-reference image quality assessment and the role of strategy","volume":"19","author":"Larson","year":"2010","journal-title":"J. Electron. Imaging."},{"key":"ref_54","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_56","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_57","unstructured":"Gu, J., Cai, H., Chen, H., Ye, X., Ren, J., and Dong, C. (2020). Image quality assessment for perceptual image restoration: A new dataset, benchmark and metric. arXiv."},{"key":"ref_58","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_59","unstructured":"Chen, G.-H., Yang, C.-L., Po, L.-M., and Xie, S.-L. (2006, January 14\u201319). Edge-based structural similarity for image quality assessment. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_60","first-page":"1500","article-title":"Image quality assessment based on gradient similarity","volume":"21","author":"Liu","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","unstructured":"Zhang, B., Sander, P.V., and Bermak, A. (2017, January 5\u20139). Gradient magnitude similarity deviation on multiple scales for color image quality assessment. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952357"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"4270","DOI":"10.1109\/TIP.2014.2346028","article-title":"VSI: A visual saliency-induced index for perceptual image quality assessment","volume":"23","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.image.2017.11.001","article-title":"A Haar wavelet-based perceptual similarity index for image quality assessment","volume":"61","author":"Reisenhofer","year":"2018","journal-title":"Signal Process. Image Commun."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5579","DOI":"10.1109\/ACCESS.2016.2604042","article-title":"Mean deviation similarity index: Efficient and reliable full-reference image quality evaluator","volume":"4","author":"Nafchi","year":"2016","journal-title":"IEEE Access"},{"key":"ref_67","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_68","unstructured":"Sheikh, H.R., and Bovik, A.C. (2022, December 18). A Visual Information Fidelity Approach to Video Quality Assessment. Available online: https:\/\/utw10503.utweb.utexas.edu\/publications\/2005\/hrs_vidqual_vpqm2005.pdf."},{"key":"ref_69","unstructured":"Mohammadi, P., Ebrahimi-Moghadam, A., and Shirani, S. (2014). Subjective and objective quality assessment of image: A survey. arXiv."},{"key":"ref_70","unstructured":"ITU-T Recommendation, P. (2008). 910. Subjective Video Quality Assessment Methods for Multimedia Applications, ITU."},{"key":"ref_71","unstructured":"ITU-R Recommendation, B.T. (1994). 814-1. Specification and Alignment Procedures for Setting of Brightness and Contrast of Displays, ITU."},{"key":"ref_72","unstructured":"ITU-R Recommendation, B.T. (1998). 1129-2. Subjective Assessment of Standard Definition Digital Television (SDTV) Systems, ITU."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Akyazi, P., Sun, H., Katto, J., and Ebrahimi, T. (2019, January 22\u201325). Perceptual quality study on deep learning based image compression. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803824"},{"key":"ref_74","first-page":"164","article-title":"Learning-based image coding: Early solutions reviewing and subjective quality evaluation","volume":"11353","author":"Ascenso","year":"2020","journal-title":"Optics, Photonics and Digital Technologies for Imaging Applications"},{"key":"ref_75","unstructured":"Egger-Lampl, S., Redi, J., Ho\u00dffeld, T., Hirth, M., M\u00f6ller, S., Naderi, B., Keimel, C., and Saupe, D. (2017). Evaluation in the crowd. Crowdsourcing and human-centered experiments, Springer."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, K.-T., Wu, C.-C., Chang, Y.-C., and Lei, C.-L. (2009, January 19\u201324). A crowdsourceable QoE evaluation framework for multimedia content. Proceedings of the 17th ACM International Conference on Multimedia, Beijing, China.","DOI":"10.1145\/1631272.1631339"},{"key":"ref_77","first-page":"512","article-title":"Overview of the JPEG XS core coding system subjective evaluations","volume":"10752","author":"Mahmoudpour","year":"2018","journal-title":"Applications of Digital Image Processing XLI"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1002\/jsid.297","article-title":"A new standard method of subjective assessment of barely visible image artifacts and a new public database","volume":"22","author":"Hoffman","year":"2014","journal-title":"J. Soc. Inf. Disp.."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"485","DOI":"10.2307\/1419876","article-title":"The staircase-method in psychophysics","volume":"75","author":"Cornsweet","year":"1962","journal-title":"Am. J. Psychol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"040901","DOI":"10.1117\/1.JEI.27.4.040901","article-title":"JPEG-1 standard 25 years: Past, present, and future reasons for a success","volume":"27","author":"Hudson","year":"2018","journal-title":"J. Electron. Imaging."},{"key":"ref_81","unstructured":"(2022, October 18). Libjpeg-Turbo. Available online: https:\/\/libjpeg-turbo.org\/Main\/HomePage."},{"key":"ref_82","unstructured":"JPEG\u2014JPEG, A.I. (2022, November 01). Available online: https:\/\/jpeg.org\/jpegai\/dataset.html."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"7371","DOI":"10.1109\/ACCESS.2017.2694038","article-title":"A method for fast multi-exposure image fusion","volume":"5","author":"Choi","year":"2017","journal-title":"IEEE Access"},{"key":"ref_84","unstructured":"(2022). ICQ JPEG AI Common Training and Test Conditions (Standard No. ISO\/IEC JTC 1\/SC29\/WG1 N100106)."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:14:02Z","timestamp":1760120042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,23]]},"references-count":84,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031297"],"URL":"https:\/\/doi.org\/10.3390\/s23031297","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,23]]}}}