{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:29:35Z","timestamp":1774880975861,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T00:00:00Z","timestamp":1577750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Image quality assessment (IQA) is a fundamental technology for image applications that can help correct low-quality images during the capture process. The ability to expand distorted images and create human visual system (HVS)-aware labels for training is the key to performing IQA tasks using deep neural networks (DNNs), and image quality is highly sensitive to changes in entropy. Therefore, a new data expansion method based on entropy and guided by saliency and distortion is proposed in this paper. We introduce saliency into a large-scale expansion strategy for the first time. We regionally add distortion to a set of original images to obtain a distorted image database and label the distorted images using entropy. The careful design of the distorted images and the entropy-based labels fully reflects the influences of both saliency and distortion on quality. The expanded database plays an important role in the application of a DNN for IQA. Experimental results on IQA databases demonstrate the effectiveness of the expansion method, and the network\u2019s prediction effect on the IQA databases is found to be improved compared with its predecessor algorithm. Therefore, we conclude that a data expansion approach that fully reflects HVS-aware quality factors is beneficial for IQA. This study presents a novel method for incorporating saliency into IQA, namely, representing it as regional distortion.<\/jats:p>","DOI":"10.3390\/e22010060","type":"journal-article","created":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T07:40:55Z","timestamp":1577778055000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Entropy Based Data Expansion Method for Blind Image Quality Assessment"],"prefix":"10.3390","volume":"22","author":[{"given":"Xiaodi","family":"Guan","sequence":"first","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Guangdong Xi\u2019an Jiaotong University Academy, Foshan 528300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"He","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1634","authenticated-orcid":false,"given":"Fan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,31]]},"reference":[{"key":"ref_1","unstructured":"Lin, Z., and Li, H. (October, January 30). SR-SIM: A fast and high performance IQA index based on spectral residual. Proceedings of the 19th IEEE International Conference on Image Processing, Orlando, FL, USA."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kim, J., and Lee, S. (2017, January 21\u201326). Deep learning of human visual sensitivity in image quality assessment framework. Proceedings of the CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.213"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1109\/TIP.2016.2601821","article-title":"Reduced-reference quality assessment based on the entropy of DWT coefficients of locally weighted gradient magnitudes","volume":"25","author":"Golestaneh","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1109\/TMM.2017.2764329","article-title":"A cost-constrained video quality satisfaction study on mobile devices","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fu, J., Zheng, H., and Mei, T. (2017, January 21\u201326). Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.476"},{"key":"ref_6","first-page":"2338","article-title":"R-FCN: Object detection via region-based fully convolutional networks","volume":"17","author":"Dai","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_7","unstructured":"Xie, S., and Tu, Z. (2017, January 21\u201326). Holistically-nested edge detection. Proceedings of the CVPR, Honolulu, HI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rastgoo, R., Kiani, K., and Escalera, S. (2018). Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine. Entropy, 20.","DOI":"10.3390\/e20110809"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"123788","DOI":"10.1109\/ACCESS.2019.2938900","article-title":"A Survey of DNN Methods for Blind Image Quality Assessment","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., and Doermann, D. (2014, January 24\u201327). Convolutional neural networks for no-reference image quality assessment. Proceedings of the CVPR, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.224"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vu, E., and Chandler, D.-M. (2008, January 24\u201326). Visual fixation patterns when judging image quality: Effects of distortion type, amount, and subject experience. Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation, Santa Fe, NM, USA.","DOI":"10.1109\/SSIAI.2008.4512288"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, F., Zhang, W., and He, L. (2018). Blind Image Quality Assessment of Natural Scenes Based on Entropy Differences in the DCT Domain. Entropy, 20.","DOI":"10.3390\/e20110885"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ren, Y., Sun, L., Wu, G., and Huang, W. (2017, January 23\u201325). DIBR-synthesized image quality assessment based on local entropy analysis. Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science, Xi\u2019an, China.","DOI":"10.1109\/FADS.2017.8253200"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F. (2009, January 20\u201326). ImageNet: A large-scale hierarchical image database. Proceedings of the CVPR, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1202213","DOI":"10.1109\/TIP.2017.2774045","article-title":"End-to-End blind image quality assessment using deep neural networks","volume":"27","author":"Ma","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TIP.2017.2760518","article-title":"Deep neural networks for no-reference and full-reference image quality assessment","volume":"27","author":"Bosse","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Takeuchi, M., and Katto, J. (2017, January 11\u201313). A Pre-Saliency Map Based Blind Image Quality Assessment via Convolutional Neural Networks. Proceedings of the 2017 IEEE International Symposium on Multimedia (ISM), Taichung, Taiwan.","DOI":"10.1109\/ISM.2017.21"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTSP.2016.2639328","article-title":"Fully deep blind image quality predictor","volume":"11","author":"Kim","year":"2017","journal-title":"IEEE J. Sel. Topics Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TNNLS.2018.2829819","article-title":"Deep CNN-based blind image quality predictor","volume":"30","author":"Kim","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Weijer, J., and Bagdanov, A. (2017, January 22\u201329). RankIQA: Learning from ranking for no-reference image quality assessment. Proceedings of the ICCV, Venice, Italy.","DOI":"10.1109\/ICCV.2017.118"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TCSVT.2011.2133770","article-title":"Visual attention in objective image quality assessment: Based on eye-tracking data","volume":"21","author":"Liu","year":"2011","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TIP.2015.2500021","article-title":"Massive online crowdsourced study of subjective and objective picture quality","volume":"25","author":"Ghadiyaram","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., and Torr, P. (2017, January 21\u201326). Deeply supervised salient object detection with short connections. Proceedings of the CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.563"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","article-title":"Waterloo exploration database: New challenges for image quality assessment models","volume":"26","author":"Ma","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","first-page":"19","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. Imag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jayaraman, D., Mittal, A., Moorthy, A.-K., and Bovik, A.-C. (2012, January 4\u20137). Objective quality assessment of multiply distorted images. Proceedings of the 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2012.6489321"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the ACM International Conference Multimedia, Orlando, FlL, USA.","DOI":"10.1145\/2647868.2654889"},{"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","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 CVPR, Providence, RI, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u2018completely blind\u2019 image quality analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1167\/17.1.32","article-title":"Perceptual quality prediction on authentically distorted images using a bag of features approach","volume":"17","author":"Ghadiyaram","year":"2017","journal-title":"J. Vis."},{"key":"ref_34","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.-E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the NIPS, Lake Tahoe, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the CVPR, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/TPAMI.2010.70","article-title":"Learning to detect a salient object","volume":"33","author":"Liu","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/1\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:47:15Z","timestamp":1760190435000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/1\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,31]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["e22010060"],"URL":"https:\/\/doi.org\/10.3390\/e22010060","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,31]]}}}