A no-reference image quality assessment metric for wood images

Rajagopal, Heshalini and Mokhtar, Norrima and Khairuddin, Anis Salwa Mohd and Khairunizam, Wan and Ibrahim, Zuwairie and Bin Adam, Asrul and Mahiyidin, Wan Amirul Bin Wan Mohd (2021) A no-reference image quality assessment metric for wood images. Journal of Robotics Networking and Artificial Life, 8 (2). pp. 127-133. ISSN 2352-6386, DOI https://doi.org/10.2991/jrnal.k.210713.012.

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Image Quality Assessment (IQA) is a vital element in improving the efficiency of an automatic recognition system of various wood species. There is a need to develop a No-Reference IQA (NR-IQA) system as a perfect and distortion free wood images may be impossible to be acquired in the dusty environment in timber factories. To the best of our knowledge, there is no NR-IQA developed for wood images specifically. Therefore, a Gray Level Co-Occurrence Matrix (GLCM) and Gabor features-based NR-IQA (GGNR-IQA) metric is proposed to assess the quality of wood images. The proposed metric is developed by training the support vector machine regression with GLCM and Gabor features calculated for wood images together with scores obtained from subjective evaluation. The proposed IQA metric is compared with a widely used NR-IQA metric, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full Reference-IQA (FR-IQA) metrics. Results shows that the proposed NR-IQA metric outperforms the BRISQUE and the FR-IQA metrics. Moreover, the proposed NR-IQA metric is beneficial in wood industry as a distortion free reference image is not needed to evaluate the wood images. (C) 2021 The Authors. Published by Atlantis Press International B.V.

Item Type: Article
Uncontrolled Keywords: Wood images;GLCM;Gabor;GGNR-IQA;NR-IQA
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 07 Sep 2022 04:57
Last Modified: 07 Sep 2022 04:57
URI: http://eprints.um.edu.my/id/eprint/34813

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