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Thereinto, image statistical comparison is a classic paradigm, while the performance is limited by the representation ability of visual descriptors. Deep features as visual descriptors have advanced IQA in recent research, but they are discovered to be highly texture-biased and lack shape-bias. On this basis, we find out that image shape and texture cues respond differently toward distortions, and the absence of either one results in an incomplete image representation. Therefore, to formulate a well-rounded statistical description for images, we utilize the shape-biased and texture-biased deep features produced by Deep Neural Networks (DNNs) simultaneously. More specifically, we design a Shape-Texture Adaptive Fusion (STAF) module to merge shape and texture information, based on which we formulate quality-relevant image statistics. The perceptual quality is quantified by the variant Mahalanobis distance between the inner and outer Deep Shape-Texture Statistics (DSTS), wherein the inner and outer statistics respectively describe the quality fingerprints of the distorted image and natural images. The proposed DSTS delicately utilizes shape-texture statistical relations between different data scales in the deep domain and achieves state-of-the-art (SOTA) quality prediction performance on images with artificial and authentic distortions.<\/jats:p>","DOI":"10.1145\/3694977","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T13:43:58Z","timestamp":1726839838000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4725-7586","authenticated-orcid":false,"given":"Yixuan","family":"Li","sequence":"first","affiliation":[{"name":"City University of Hong Kong, Kowloon, Hong Kong SAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6636-522X","authenticated-orcid":false,"given":"Peilin","family":"Chen","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, Hong Kong SAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0894-0561","authenticated-orcid":false,"given":"Hanwei","family":"Zhu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, Hong Kong SAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2900-7313","authenticated-orcid":false,"given":"Keyan","family":"Ding","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9069-8796","authenticated-orcid":false,"given":"Leida","family":"Li","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3583-959X","authenticated-orcid":false,"given":"Shiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, Hong Kong SAR"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"e_1_3_1_2_1","first-page":"126","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops","author":"Agustsson Eirikur","year":"2017","unstructured":"Eirikur Agustsson and Radu Timofte. 2017. 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