Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images
Image Quality Assessment (IQA) is an imperative element in improving the effectiveness of an automatic wood recognition system. There is a need to develop a No-Reference-IQA (NR-IQA) system as a distortion free wood images are impossible to be acquired in the dusty environment in timber factories. T...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
ALife Robotics Corp. Ltd.
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34331/ http://umpir.ump.edu.my/id/eprint/34331/7/Gray%20Level%20Co-Occurrence%20Matrix.pdf |
| _version_ | 1848824478317936640 |
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| author | Heshalini, Rajagopal Norrima, Mokhtar Anis Salwa, Mohd Khairuddin Wan Khairunizam, Wan Ahmad Zuwairie, Ibrahim Asrul, Adam Wan Amirul, Wan Mohd Mahiyidin |
| author_facet | Heshalini, Rajagopal Norrima, Mokhtar Anis Salwa, Mohd Khairuddin Wan Khairunizam, Wan Ahmad Zuwairie, Ibrahim Asrul, Adam Wan Amirul, Wan Mohd Mahiyidin |
| author_sort | Heshalini, Rajagopal |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Image Quality Assessment (IQA) is an imperative element in improving the effectiveness of an automatic wood recognition system. There is a need to develop a No-Reference-IQA (NR-IQA) system as a distortion free wood images are impossible to be acquired in the dusty environment in timber factories. Therefore, a Gray Level Co- Occurrence Matrix (GLCM) and Gabor features-based NR-IQA, GGNR-IQA algorithm is proposed to evaluate the quality of wood images. The proposed GGNR-IQA algorithm is compared with a well-known NR-IQA, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full-Reference-IQA (FR-IQA) algorithms, Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), Feature SIMilarity (FSIM), Information Weighted SSIM (IW-SSIM) and Gradient Magnitude Similarity Deviation (GMSD). Results shows that the GGNR-IQA algorithm outperforms the NR-IQA and FR-IQAs. The GGNR-IQA algorithm is beneficial in wood industry as a distortion free reference image is not required to pre-process wood images. |
| first_indexed | 2025-11-15T03:13:40Z |
| format | Conference or Workshop Item |
| id | ump-34331 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:13:40Z |
| publishDate | 2021 |
| publisher | ALife Robotics Corp. Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-343312022-11-11T08:32:32Z http://umpir.ump.edu.my/id/eprint/34331/ Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images Heshalini, Rajagopal Norrima, Mokhtar Anis Salwa, Mohd Khairuddin Wan Khairunizam, Wan Ahmad Zuwairie, Ibrahim Asrul, Adam Wan Amirul, Wan Mohd Mahiyidin TK Electrical engineering. Electronics Nuclear engineering Image Quality Assessment (IQA) is an imperative element in improving the effectiveness of an automatic wood recognition system. There is a need to develop a No-Reference-IQA (NR-IQA) system as a distortion free wood images are impossible to be acquired in the dusty environment in timber factories. Therefore, a Gray Level Co- Occurrence Matrix (GLCM) and Gabor features-based NR-IQA, GGNR-IQA algorithm is proposed to evaluate the quality of wood images. The proposed GGNR-IQA algorithm is compared with a well-known NR-IQA, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full-Reference-IQA (FR-IQA) algorithms, Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), Feature SIMilarity (FSIM), Information Weighted SSIM (IW-SSIM) and Gradient Magnitude Similarity Deviation (GMSD). Results shows that the GGNR-IQA algorithm outperforms the NR-IQA and FR-IQAs. The GGNR-IQA algorithm is beneficial in wood industry as a distortion free reference image is not required to pre-process wood images. ALife Robotics Corp. Ltd. 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34331/7/Gray%20Level%20Co-Occurrence%20Matrix.pdf Heshalini, Rajagopal and Norrima, Mokhtar and Anis Salwa, Mohd Khairuddin and Wan Khairunizam, Wan Ahmad and Zuwairie, Ibrahim and Asrul, Adam and Wan Amirul, Wan Mohd Mahiyidin (2021) Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images. In: Proceeding of the 2021 International Conference on Artificial Life and Robotics (ICAROB2021) , 21 - 24 January 2021 . pp. 736-741.. ISBN 978-4-9908350-6-4 (Published) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Heshalini, Rajagopal Norrima, Mokhtar Anis Salwa, Mohd Khairuddin Wan Khairunizam, Wan Ahmad Zuwairie, Ibrahim Asrul, Adam Wan Amirul, Wan Mohd Mahiyidin Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images |
| title | Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images |
| title_full | Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images |
| title_fullStr | Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images |
| title_full_unstemmed | Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images |
| title_short | Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images |
| title_sort | gray level co-occurrence matrix (glcm) and gabor features based no-reference image quality assessment for wood images |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/34331/ http://umpir.ump.edu.my/id/eprint/34331/7/Gray%20Level%20Co-Occurrence%20Matrix.pdf |