Subjective Score Predictor: A New Evaluation Function of Distorted Image Quality

Image quality assessment (IQA) is a method to evaluate the perceptual performance of image. Many objective IQA algorithms are developed from the objective comparison of image features, which are mainly trained and evaluated from the ground truth of subjective scores. Due to the inconsistent experiment conditions and cumbersome observing processes of subjective experiments, it is imperative to generate the ground truth for IQA research via objective computation methods. In this paper, we propose a subjective score predictor (SSP) aiming to provide the ground truth of IQA datasets. In perfect accord with distortion information, the distortion strength of distorted image is employed as a dependent parameter. To further be consistent with subjective opinion, on the one hand, the subjective score of source image is viewed as a quality base value, and, on the other hand, we integrate the distortion parameter and the quality base value into a human visual model function to obtain the final SSP value. Experimental results demonstrate the advantages of the proposed SSP in the following aspects: effective performance to reflect the distortion strength, competitive ground truth, and valid evaluation for objective IQA methods as well as subjective scores.