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The human eye cannot perceive small pixel changes in images or videos until acertain threshold of distortion. In the context of video compression, JustNoticeable Difference (JND) is the smallest distortion level from which thehuman eye can perceive the difference between reference video and thedistorted/compressed one. Satisfied-User-Ratio (SUR) curve is the complementarycumulative distribution function of the individual JNDs of a viewer group.However, most of the previous works predict each point in SUR curve by usingfeatures both from source video and from compressed videos with assumption thatthe group-based JND annotations follow Gaussian distribution, which is neitherpractical nor accurate. In this work, we firstly compared various commonfunctions for SUR curve modeling. Afterwards, we proposed a novelparameter-driven method to predict the video-wise SUR from video features.Besides, we compared the prediction results of source-only features based(SRC-based) models and source plus compressed videos features (SRC+PVS-based)models.

On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video