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Gray level co-occurrence matrix (Glcm) and gabor features based no-reference image quality assessment for wood images

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cris.author.scopus-author-id 57188756370
cris.author.scopus-author-id 25930072600
cris.author.scopus-author-id 36019257000
cris.author.scopus-author-id 57200576499
cris.author.scopus-author-id 7005685730
cris.author.scopus-author-id 36197989900
cris.author.scopus-author-id 57224972737
dc.contributor.author Rajagopal H.
dc.contributor.author Mokhtar N.
dc.contributor.author Khairuddin A.S.M.
dc.contributor.author Khairunizam W.
dc.contributor.author Ibrahim Z.
dc.contributor.author Adam A.B.
dc.contributor.author Mahiyidin W.A.B.W.M.
dc.date.accessioned 2024-09-28T14:44:22Z
dc.date.available 2024-09-28T14:44:22Z
dc.date.issued 2021-01-01
dc.description.abstract 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.
dc.identifier.scopus 2-s2.0-85108851019
dc.identifier.uri https://hdl.handle.net/20.500.14170/5627
dc.relation.grantno undefined
dc.relation.ispartof Proceedings of International Conference on Artificial Life and Robotics
dc.relation.ispartofseries Proceedings of International Conference on Artificial Life and Robotics
dc.subject Gabor | GGNR-IQA | GLCM | NR-IQA | Wood images
dc.title Gray level co-occurrence matrix (Glcm) and gabor features based no-reference image quality assessment for wood images
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.endPage 741
oaire.citation.startPage 736
oaire.citation.volume 2021
oairecerif.affiliation.orgunit Universiti Malaya
oairecerif.affiliation.orgunit Universiti Malaya
oairecerif.affiliation.orgunit Universiti Malaya
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Pahang Al-Sultan Abdullah
oairecerif.affiliation.orgunit Universiti Malaysia Pahang Al-Sultan Abdullah
oairecerif.affiliation.orgunit Universiti Malaya
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person.identifier.scopus-author-id 57188756370
person.identifier.scopus-author-id 25930072600
person.identifier.scopus-author-id 36019257000
person.identifier.scopus-author-id 57200576499
person.identifier.scopus-author-id 7005685730
person.identifier.scopus-author-id 36197989900
person.identifier.scopus-author-id 57224972737
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