An efficient unsupervised image quality metric with application for condition recognition in kiln. (January 2022)

• Record Type: Journal Article
• Authors: Wu, Leyuan; Zhang, Xiaogang; Chen, Hua; Zhou, Yicong; Wang, Lianhong; Wang, Dingxiang
• Is Part Of: Engineering applications of artificial intelligence. Volume 107(2022)
• Publication Date: 2022-01
• Subjects: Blind image quality assessment (BIQA) -- Textural intensity -- IQA-based application -- Sintering condition recognition; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104547
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, we propose an unsupervised textural-intensity-based natural image quality evaluator (TI-NIQE) by modelling the texture, structure and naturalness of an image. In detail, an effective quality-aware feature named as textural intensity (TI) is proposed in this paper to detect image texture. The image structure is captured by the distribution of gradients and basis images. The naturalness is characterized through the distributions of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Furthermore, a new application pattern of image quality assessment (IQA) measures is proposed by taking the quality scores as the essential input of the recognition model. Using statistics of video quality scores computed by TI-NIQE as input features, an automatic IQA-based visual recognition model is proposed for the condition recognition in rotary kiln. Extensive experiments on benchmark datasets demonstrate that TI-NIQE shows better performance both in accuracy and computational complexity than other state-of-the-art unsupervised IQA methods, and experimental results on real-world data show that the recognition model has high prediction accuracy for condition recognition in rotary kiln. Highlights: Proposed an effective image textural intensity detection method. Proposed a low computational complexity yet has high prediction accuracy unsupervised BIQA method. Realized the quality evaluation of the flame image. Proposed a robust sintering condition recognition model in rotary kiln.