Hybrid Distortion Ranking Tuned Bitstream-layer Video Quality Assessment

No-reference bitstream-layer video quality assessment is very important and practical for monitoring the perceptual experience of end-users and facilitating network maintenance. For the nowadays pervasive IPTV and mobile streaming services, in addition to quality degradation due to lossy compression, the unreliable transmission mechanism (i.e. UDP/IP) often leads to quality degradation due to packet loss. Different technical solutions bring in different types of visual artifacts. In this paper, we proposed a Hybrid Distortion Ranking (HDR) based bitstream-layer quality assessment model, whose artifact combination framework is based on the ranked linear combination operation. The model can predict the perceived quality of a video with sufficient accuracy when the video is distorted by compression artifacts, slicing artifacts, freezing (with frame skipping) artifacts, or their combinations. The core algorithms of the model were adopted into ITU-T Recommendation, P.1202.1 and P.1202.2. Furthermore, with respect to the three different types of artifacts, we compared the proposed no-reference HDR model with some state-of-the-art full-reference perceptual quality assessment models including VQM (i.e. ITU-T Rec. J.144), SSIM, MS-SSIM, VIF and the widely used metric, PSNR. We also compared our HDR model with the top performing no-reference models including BRISQUE and video BLINDS. The experiment results demonstrate the efficiency of our HDR model.