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Proceedings Paper

Subjective evaluation of H.265/HEVC based dynamic adaptive video streaming over HTTP (HEVC-DASH)
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Paper Abstract

The Dynamic Adaptive Streaming over HTTP (DASH) standard is becoming increasingly popular for real-time adaptive HTTP streaming of internet video in response to unstable network conditions. Integration of DASH streaming techniques with the new H.265/HEVC video coding standard is a promising area of research. The performance of HEVC-DASH systems has been previously evaluated by a few researchers using objective metrics, however subjective evaluation would provide a better measure of the user’s Quality of Experience (QoE) and overall performance of the system. This paper presents a subjective evaluation of an HEVC-DASH system implemented in a hardware testbed. Previous studies in this area have focused on using the current H.264/AVC (Advanced Video Coding) or H.264/SVC (Scalable Video Coding) codecs and moreover, there has been no established standard test procedure for the subjective evaluation of DASH adaptive streaming. In this paper, we define a test plan for HEVC-DASH with a carefully justified data set employing longer video sequences that would be sufficient to demonstrate the bitrate switching operations in response to various network condition patterns. We evaluate the end user’s real-time QoE online by investigating the perceived impact of delay, different packet loss rates, fluctuating bandwidth, and the perceived quality of using different DASH video stream segment sizes on a video streaming session using different video sequences. The Mean Opinion Score (MOS) results give an insight into the performance of the system and expectation of the users. The results from this study show the impact of different network impairments and different video segments on users’ QoE and further analysis and study may help in optimizing system performance.

Paper Details

Date Published: 27 February 2015
PDF: 11 pages
Proc. SPIE 9400, Real-Time Image and Video Processing 2015, 94000B (27 February 2015); doi: 10.1117/12.2080365
Show Author Affiliations
Iheanyi Irondi, Univ. of the West of Scotland (United Kingdom)
Qi Wang, Univ. of the West of Scotland (United Kingdom)
Christos Grecos, Univ. of the West of Scotland (United Kingdom)


Published in SPIE Proceedings Vol. 9400:
Real-Time Image and Video Processing 2015
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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