On the improvement of no-reference mean opinion score estimation accuracy by following a frame-level regression approach

In order to estimate subjective video quality, we usually deal with a large number of features and a small sample set. Applying regression on complex datasets may lead to imprecise solutions due to possibly irrelevant or noisy features as well as the effect of overfitting. In this work, we propose a No-Reference (NR) method for the estimation of the quality of videos that are impaired by both compression artifacts and packet losses. Particularly, in an effort to establish a robust regression model that generalizes well to unknown data and to increase Mean Opinion Score (MOS) estimation accuracy, we propose a frame-level MOS estimation approach, where the MOS estimate of a sequence is obtained by averaging the per-frame MOS estimates, instead of performing regression directly at the sequence-level. Since it is impractical to obtain the actual per-frame MOS values through subjective experiments, we propose an objective metric able to do this task. Thus, our proposed NR method has the dual benefit of offering improved sequence-level MOS estimation accuracy, while giving an indication of the relative quality of each individual video frame.