Compressed image quality assessment using stacking
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914664221245440 |
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| author | Hosseini-Benvidi, S. Farhad Motamednia, Hossein Mansouri, Azadeh Raei, Mohammadreza Mahmoudi-Aznaveh, Ahmad |
| author_facet | Hosseini-Benvidi, S. Farhad Motamednia, Hossein Mansouri, Azadeh Raei, Mohammadreza Mahmoudi-Aznaveh, Ahmad |
| contents | It is well-known that there is no universal metric for image quality evaluation. In this case, distortion-specific metrics can be more reliable. The artifact imposed by image compression can be considered as a combination of various distortions. Depending on the image context, this combination can be different. As a result, Generalization can be regarded as the major challenge in compressed image quality assessment. In this approach, stacking is employed to provide a reliable method. Both semantic and low-level information are employed in the presented IQA to predict the human visual system. Moreover, the results of the Full-Reference (FR) and No-Reference (NR) models are aggregated to improve the proposed Full-Reference method for compressed image quality evaluation. The accuracy of the quality benchmark of the clic2024 perceptual image challenge was achieved 79.6\%, which illustrates the effectiveness of the proposed fusion-based approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00993 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compressed image quality assessment using stacking Hosseini-Benvidi, S. Farhad Motamednia, Hossein Mansouri, Azadeh Raei, Mohammadreza Mahmoudi-Aznaveh, Ahmad Image and Video Processing Computer Vision and Pattern Recognition It is well-known that there is no universal metric for image quality evaluation. In this case, distortion-specific metrics can be more reliable. The artifact imposed by image compression can be considered as a combination of various distortions. Depending on the image context, this combination can be different. As a result, Generalization can be regarded as the major challenge in compressed image quality assessment. In this approach, stacking is employed to provide a reliable method. Both semantic and low-level information are employed in the presented IQA to predict the human visual system. Moreover, the results of the Full-Reference (FR) and No-Reference (NR) models are aggregated to improve the proposed Full-Reference method for compressed image quality evaluation. The accuracy of the quality benchmark of the clic2024 perceptual image challenge was achieved 79.6\%, which illustrates the effectiveness of the proposed fusion-based approach. |
| title | Compressed image quality assessment using stacking |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.00993 |