An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis

Abstract

No-reference image quality assessment (NR-IQA) uses only the test image for its quality assessment, and as video is essentially comprised of image frames with additional temporal dimension, video quality assessment (VQA) requires a thorough understanding of image quality assessment metrics and models. Therefore, in order to identify features that deteriorate video quality, a fundamental analysis of spatial and temporal artifacts with respect to individual video frames needs to be performed. Existing IQA and VQA metrics are primarily for capturing few distortions and hence may not be good for all types of images and videos. In this paper, we propose an NR-IQA model by combining existing three methods (namely NIQE, BRISQUE and BLIINDS-II) using multi-linear regression. We also present a holistic no-reference video quality assessment (NR-VQA) model by exploring quantification of certain distortions like ringing, frame difference, blocking, clipping and contrast in video frames. For the proposed NR-IQA model, the results represent improved performance as compared to the state-of-the-art methods and it requires very low fraction of samples for training to provide a consistent accuracy over different training-to-testing ratios. The performance of NR-VQA model is examined using a simple neural network model to attain high value of goodness of fit.