No-reference super-resolution image quality assessment method using multi-layer perceptron regression

Autor:	ZHU Danni, XU Xiaohua, HE Jingjing, WANG Chen, ZHANG Kaibing
Jazyk:	čínština
Rok vydání:	2022
Předmět:	depth feature multi-layer perceptron super-resolution image quality assessment regression model mean opinion score Materials of engineering and construction. Mechanics of materials TA401-492 Environmental engineering TA170-171
Zdroj:	Xi'an Gongcheng Daxue xuebao, Vol 36, Iss 5, Pp 70-78 (2022)
Druh dokumentu:	article
ISSN:	1674-649X 1674-649x
DOI:	10.13338/j.issn.1674-649x.2022.05.010
Popis:	In order to solve the problem of poor consistency between the traditional super-resolution image quality assessment (SRIQA) index and human subjective perception, this paper presents a reference free super-resolution image quality evaluation method by using multi-layer perceptron (MLP). This method used the pre trained VGG16 network to extract the perceptual quality features of the super-resolution image, and established the regression model between the perceptual quality features of the super-resolution image and the corresponding subjective quality score through MLP. Experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of the proposed algorithm on the public super-resolution image data set exceed 0.95, which is significantly superior to other existing image quality evaluation methods, and has higher consistency with human subjective perception.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/66b84138996f40c5a54945858250ba95 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF