Selecting Low-level Features for Image Quality Assessment by Statistical Methods

Atidel Lahouhou, Emmanuel Viennet, Azeddine Beghdadi

Abstract


Image quality assessment is an important component in every image processing system where the last link of the chain is the human observer. This domain is of increasing interest, in particular in the context of image compression where coding scheme optimization is based on the distortion measure. Many objective image quality measures have been proposed in the literature and validated by comparing them to the Mean Opinion Score (MOS). We propose in this paper an empirical study of several indicators and show how one can improve the performances by combining them. We learn a regularized regression model and apply variable selection techniques to automatically find the most relevant indicators. Our technique enhances the state of the art results on two publicly available databases.

Keywords


Image quality assessment, perceptual

Full Text:

PDF


DOI: https://doi.org/10.2498/cit.1001822

Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Crossref Similarity Check logo

Crossref logologo_doaj