caa a22 4500 605447586 CHVBK 20210128100132.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1795-z doi (NATIONALLICENCE)springer-10.1007/s11042-013-1795-z Encoding and video content based HEVC video quality prediction [Elektronische Daten] [Louis Anegekuh, Lingfen Sun, Emmanuel Ifeachor] 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. Springer Science+Business Media New York, 2013 QoE nationallicence HEVC nationallicence MV nationallicence CVQP nationallicence Video quality evaluation nationallicence Motion amount nationallicence Video complexity nationallicence Anegekuh Louis Centre for Signal Processing and Multimedia Communication, Plymouth University, Drake Circus, PL4 8AA, Plymouth, UK aut Sun Lingfen Centre for Signal Processing and Multimedia Communication, Plymouth University, Drake Circus, PL4 8AA, Plymouth, UK aut Ifeachor Emmanuel Centre for Signal Processing and Multimedia Communication, Plymouth University, Drake Circus, PL4 8AA, Plymouth, UK aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3715-3738 1380-7501 74:11<3715 2015 74 11042 https://doi.org/10.1007/s11042-013-1795-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1795-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Anegekuh Louis Centre for Signal Processing and Multimedia Communication, Plymouth University, Drake Circus, PL4 8AA, Plymouth, UK aut NATIONALLICENCE 700 1- Sun Lingfen Centre for Signal Processing and Multimedia Communication, Plymouth University, Drake Circus, PL4 8AA, Plymouth, UK aut NATIONALLICENCE 700 1- Ifeachor Emmanuel Centre for Signal Processing and Multimedia Communication, Plymouth University, Drake Circus, PL4 8AA, Plymouth, UK aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3715-3738 1380-7501 74:11<3715 2015 74 11042