VNU-UET Repository

Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding

Hoang, Van Xiem (2020) Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding. Electronics, 9 (6). pp. 1-16. ISSN 2079-9292

Download (4MB) | Preview


Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve high quality inter prediction, we propose a long-term reference coding method, which adaptively exploits the temporal correlation among frames in surveillance video. Second, to optimize the SSVC compression performance, we design a quantization parameter adaptation mechanism in which the relationship between SSVC rate-distortion (RD) performance and the quantization parameter is statistically modeled by a fourth-order polynomial function. Afterwards, an appropriate quantization parameter is derived for frames at long-term reference position. Experiments conducted for a common set of surveillance videos have shown that the proposed SSVC significantly outperforms the relevant SHVC standard, notably by around 6.9% and 12.6% bitrate saving for the low delay (LD) and random access (RA) coding configurations, respectively while still providing a similar perceptual decoded frame quality.

Item Type: Article
Subjects: Electronics and Communications
Electronics and Communications > Electronics and Computer Engineering
Divisions: Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr. Xiem HoangVan
Date Deposited: 18 Jul 2020 02:46
Last Modified: 18 Jul 2020 02:46

Actions (login required)

View Item View Item