A Generic Method to Improve No-Reference Image Blur Metric Accuracy in Video Contents


We present in this work a generic and effective method to increase the prediction accuracy of no-reference image/video blur assessment facing the real-world content diversity. We demonstrate that benchmarking no reference image blur metrics, fitting a single logistic function to map the objective predictions to subjective scores in the well-known databases like LIVE or TID2008/2013, introduce biased fitting results towards better predictions only in the central part of the score scale. We find out that a multi-fitting approach, using the correlation parameters between subjective scores and objective predictions for content clustering and then conducting logistic fitting for each content type, can evidently improve the metric prediction accuracy in the full score scale. Besides, the overall prediction variance is also reduced with the proposed scheme, presenting more consistent results insensitive of content variation. We prove that the proposed method is of practical meaning to facilitate blur assessment techniques validated on limited databases to the vastly abundant real-life content types.

2017 IEEE Visual Communications and Image Processing (VCIP)
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.