Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding

Abstract

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.

Efficient High-Resolution Video Compression Scheme Using Background and Foreground Layers

“…The performance comparison among proposed method, traditional SHVC and reference method [18] is presented in Fig. 4 for 'Community', 'Shark Encounter', 'KiteFlite' and 'Broadway' video sequence.…”

“…Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. HoangVan [18].…”

“…By applying the decoded information achieved from both BL and EL, the JLP method could create a new prediction picture. The work in [18] proposed an adaptive long-term reference selection algorithm for surveillance cameras using scalable video coding. Based on the content analysis of a video sequence, this approach selected a coded picture as long-term reference picture.…”

A Trellis Based Temporal Rate Allocation and Virtual Reference Frames for High Efficiency Video Coding

“…In addition, to explore the effectiveness of the proposed TRA method, we conducted further experiments for relevant frame-based QP adaptation methods, notably the QPλ model proposed in [34] where the QP and λ are modeled as a linear function and our recent work in [35] where the QP and RD cost is modeled as a polynomial function. The model parameters were selected as in [34,35]. We also slightly modified the TRA to change the QP for only the Tid 0, named TRA_0.…”

“…This may come from the fact that the training set does not include any video with this content, and the VRF creation may also not work well for screen-captured videos; • Compared to other frame-based QPA algorithms, the proposed method achieved better BDBR (see Table 5). It should be noted that the QPA proposed in [35] was mainly designed for surveillance video content and its high-order polynomial model is highly sensitive to the selected parameters. Similarly, the QP-λ linear model proposed in [34] is also unable to achieve good BDBR performance even with the original model parameters used in [34] or our new parameters; • A similar compression achievement is also observed for the BDPSNR comparison.…”