Texture analysis assessment for images

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dc.contributor.authorTitijaroonroj, Taravichet
dc.contributor.authorKaewaramsri, Yothin
dc.contributor.authorSuttapakti, Ungsumalee
dc.contributor.authorWoraratpanya, Kuntpong
dc.contributor.authorKuroki, Yoshimitsu
dc.date.accessioned2026-08-06T10:16:15Z
dc.date.available2026-08-06T10:16:15Z
dc.date.issued2017-02-23
dc.description.abstractCommonly, the existing metrics such as mean square error (MSE), peak signal-To-noise ratio (PSNR), quality index (QI), structural similarity index metric (SSIM), and quality index based on local variance (QILV) use the image intensity-based statistics approach to assess the quality of distorted images. These metrics are successful in discriminating the quality of distorted images, such as de-noising, JPEG compressed, and blur images. However, they are unsuccessful in discriminating the quality of channel decomposition images. Therefore, this paper proposes the texture analysis assessment (TAA) to measure the quality of both normally distorted images and channel decomposition images. The proposed metric uses image intensity statistics in conjunction with texture analysis for quality discrimination of slightly different distorted and channel decomposition images. The texture analysis based on edge orientation is an important part employed to measure precise image errors. The experimental results illustrate that the TAA metric can evidently discriminate the quality of normally distorted images and channel decomposition images, when compared with state-of-The-Art metrics. Furthermore, the perceived visual quality and the quality value of TAA are corresponding; the lower visual quality human-eye perceives, the lower quality value TAA measures, and vice versa.
dc.identifier.citationProceedings of 2016 8th International Conference on Information Technology and Electrical Engineering Empowering Technology for Better Future Icitee 2016, 2017
dc.identifier.doi10.1109/ICITEED.2016.7863254
dc.identifier.other2-s2.0-85016055675
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7540
dc.sourceProceedings of 2016 8th International Conference on Information Technology and Electrical Engineering Empowering Technology for Better Future Icitee 2016
dc.subjectimage quality assessment (IQA)
dc.subjectmean square error (MSE)
dc.subjectpeak signal-To-noise ratio (PSNR)
dc.subjectquality index (QI)
dc.subjectquality index based on local variance (QILV)
dc.subjectstructural similarity (SSIM)
dc.subjectTexture analysis assessment (TAA)
dc.titleTexture analysis assessment for images
dc.typeConference Paper

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