Compressed image quality assessment using stacking
This addresses the problem of reliable quality evaluation for compressed images, which is incremental as it builds on existing FR and NR models.
The paper tackled the challenge of generalizing compressed image quality assessment by using stacking to combine semantic and low-level information, achieving 79.6% accuracy on the CLIC2024 benchmark.
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.