Quality metric aggregation for HDR/WCG images

Maxime Rousselot (1, 2) , Xavier Ducloux (3) , Olivier Le Meur (1, 2) , Rémi Cozot (1, 2)

1 IRISA-D6 - MEDIA ET INTERACTIONS
2 PERCEPT - Computational Visual Perception and Applications
3 Harmonic - Harmonic

High Dynamic Range (HDR) and Wide Color Gamut (WCG) screens are able to display images with brighter and darker pixels with more vivid colors than ever. Automatically assessing the quality of these HDR/WCG images is of critical importance to evaluate the performances of image compression schemes. In recent years, full-reference metrics, such as HDR-VDP-2, PU-encoding metrics, have been designed for this purpose. However, none of these met-rics consider chromatic artifacts. In this paper, we propose our own full-reference quality metric adapted to HDR and WCG content that is sensitive to chromatic distortions. The proposed metric is based on two existing HDR quality metrics and color image features. A support vector machine regression is used to combine the aforemen-tioned features. Experimental results demonstrate the effectiveness of the proposed metric in the context of image compression.

Keywords: Image quality; High Dynamic Range; Image processing; Machine learning

Domains: Signal and Image processing

Maxime Rousselot, Xavier Ducloux, Olivier Le Meur, Rémi Cozot. Quality metric aggregation for HDR/WCG images. International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. ⟨hal-02265122⟩