No-Reference Learning-based and Human Visual-based Image Quality Assessment Metric

Christophe Charrier (1) , Abdelhakim Saadane (2) , Christine Fernandez-Maloigne (2)

1 Equipe SAFE - Laboratoire GREYC - UMR6072
2 XLIM-ASALI - Synthèse et analyse d'images

With the rapid growth of multimedia applications and technologies, objective image quality assessment (IQA) became a topic of fundamental interest. No-Reference (NR) IQA algorithms are more suitable to real-world applications where the original image is not available. In order to be more consistent with human perception, this paper proposes a new NR-IQA metric where the input image is firstly decomposed to several frequency sub-bands which mimic the human visual system (HVS). Then, the statistical features are extracted from these frequency bands and used to fit a multivariate Gaussian distribution (MVGD). Finally, the model obtained by training predicts the quality of the input image. Experimental results demonstrate the method effectiveness and show its robust-ness when tested by different databases. Moreover, the predicted quality is more consistent with human perception.

Mots clés: image quality assessment; multivariate Gaussian distribution; human visual system; visual sub-bands; no reference

Domaines: Traitement des images [eess.IV]

Christophe Charrier, Abdelhakim Saadane, Christine Fernandez-Maloigne. No-Reference Learning-based and Human Visual-based Image Quality Assessment Metric. 19th International Conference on Image Analysis and Processing, Sep 2017, Catania, Italy. ⟨hal-01595943⟩