A no reference video quality assessment metric based on the region of interest (ROI) was proposed in this paper. In the metric, objective video quality was evaluated by integrating the quality of two compressed artifacts, i.e. blurring distortion and blocking distortion. The Gaussian kernel function was used to extract the human density maps of the H.264 coding videos from the subjective eye tracking data. An objective bottom-up ROI extraction model based on magnitude discrepancy of discrete wavelet transform between two consecutive frames, center weighted color opponent model, luminance contrast model and frequency saliency model based on spectral residual was built. Then only the objective saliency maps were used to compute the objective blurring and blocking quality. The results indicate that the objective ROI extraction metric has a higher the area under the curve (AUC) value. Comparing with the conventional video quality assessment metrics which measured all the video quality frames, the metric proposed in this paper not only decreased the computation complexity, but improved the correlation between subjective mean opinion score (MOS) and objective scores.

Date Published: 8 February 2015
PDF: 14 pages
Proc. SPIE 9396, Image Quality and System Performance XII, 93960Z (8 February 2015); doi: 10.1117/12.2083892

Published in SPIE Proceedings Vol. 9396:
Image Quality and System Performance XII
Mohamed-Chaker Larabi; Sophie Triantaphillidou, Editor(s)