Compressed image quality assessment using stacking

• URL: http://arxiv.org/abs/2402.00993v1
• Date: Thu, 1 Feb 2024 20:12:26 GMT
• Title: Compressed image quality assessment using stacking
• Authors: S. Farhad Hosseini-Benvidi, Hossein Motamednia, Azadeh Mansouri, Mohammadreza Raei, Ahmad Mahmoudi-Aznaveh
• Abstract summary: Generalization can be regarded as the major challenge in compressed image quality assessment. Both semantic and low-level information are employed in the presented IQA to predict the human visual system. The accuracy of the quality benchmark of the clic2024 perceptual image challenge was achieved 79.6%.
• Score: 4.971244477217376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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