Content-adaptive Traffic Surveillance Video Coding with Extended Spatial Scalability

Abstract

Regions of interest (ROI) or visually salient regions are rarely considered in spatial scalable video coding, thus visually important content can not be better adapted to lower display resolutions. In this paper, we propose a content-adaptive spatial scalable coding for traffic surveillance video. First, the background image is extracted by an improved single Gaussian method based on the spatio-temporal model and updated from the latest static image. Then a background subtraction algorithm is present for detecting and tracking vehicles, the motion window of the leading vehicle is commonly referred to as ROI in traffic surveillance, and ROI is as a cropping window in extended spatial scalability (ESS) of the scalable video coding (SVC). Moreover, we employ a tracking-aware compression algorithm to remove more low tracking interest bit rate by ROI-based quantization strategy and frequency coefficient suppression technique, so tracking accuracy is used instead of PSNR as the compression criterion. The experimental results show that compared with conventional scaling coding the proposed algorithm can greatly improve the visual perception of the decoded base layer video with limited loss in the rate-distortion performance, and allows for about 60% bit rate savings while maintaining comparable tracking accuracy.