Foveation-based Content Adaptive Root Mean Squared Error for Video Quality Assessment

When the video is compressed and transmitted over heterogeneous networks, it is necessary to ensure the satisfying quality for the end user. Since human observers are the end users of video applications, it is very important that the human visual system (HVS) characteristics are taken into account during the video quality evaluation. This paper deals with video quality assessment (VQA) based on HVS characteristics and proposes a novel full-reference (FR) VQA metric called the Foveation-based content Adaptive Root Mean Squared Error (FARMSE). FARMSE uses several HVS characteristics that significantly influence perception of distortions in a video. Primarily these are foveated vision, reduction of the spatial acuity due to motions as well as spatial masking. Foveated vision is related to variable resolution of HVS across the viewing field, where the highest resolution is at the point of fixation. The point of fixation is projected onto the fovea – the area of retina with the highest density of photoreceptors. The part of image that falls on fovea is perceived by the highest acuity, whereas the spatial acuity decreases as the distance of the image part from the fovea increases. Spatial acuity further decreases if eyes cannot track moving objects. Both mentioned mechanisms influence contrast sensitivity of the HVS. Contrast sensitivity is frequency dependent and FARMSE uses Haar filters to utilize this dependence. Furthermore, spatial masking is implemented in each frequency channel. The FARMSE performance is compared to this of nine state-of-the-art VQA metrics on two different databases, LIVE and ECVQ. Additionally, the metrics are compared in terms of calculation complexity. The performed experiments show that FARMSE achieves high performance when predicting the quality of videos with different resolutions, degradation types and content types. FARMSE results outperform the results of most of the analyzed metrics, whereas they are comparable to these of the best publicly available metrics, including the well-known MOtion-based Video Integrity Evaluation (MOVIE) index. Besides that, FARMSE calculation complexity is significantly lower than that of the metrics comparable thereto in terms of prediction accuracy.