Compressed image quality assessment using stacking
S. Farhad Hosseini-Benvidi Hossein Motamednia Azadeh Mansouri Mohammadreza Raei Ahmad Mahmoudi-Aznaveh
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.