Encoding and video content based HEVC video quality prediction

Advances in multimedia devices and video compression techniques and the availability of increased network bandwidth in both fixed and mobile networks has increased the proliferation of multimedia applications (e.g. IPTV, video streaming and online gaming). However, this has also posed a real challenge to network and service providers to deliver these applications with an acceptable Quality of Experience (QoE). In these multimedia applications, it is highly desirable to predict and if possible control video quality to meet such QoE and user expectations. Streamed video quality is affected by both encoding and transmission processes. The impacts of these processes are content dependent. This issue has gradually been recognised in video quality modelling research in recent years. In this paper, we carried out objective and subjective tests on video sequences to investigate the impact of video content type and encoding parameter settings on HEVC video quality. Initial results show that varying video content type and encoding parameters impact video quality. Based on the test results, we developed a content-based video quality prediction (CVQP) model that takes into account HEVC encoding parameter such as Quantization Parameter (QP) and video content type (characterised by motion activities and complexity of video sequences). We achieved an accuracy of 92 % for the test dataset when model predicted PSNR values were compared with full reference PSNR measurements. The performance of the model was also evaluated by comparing predicted PSNR with those of Double Stimulus Impairment Scale (DSIS) subjective quality ratings. Results show a good correlation between actual MOS and predicted PSNR. The proposed model could be used by content providers to determine the initial quality of videos based on QP and content type.