Provided by: Institute of Electrical & Electronic Engineers
This paper analyzes and compares the rate-accuracy and rate-energy characteristics of various video rate adaptation techniques in computer vision applications. The analyzed rate adaptation techniques include spatial, spatial with up-scaling, temporal, and Signal-to-Noise Ratio (SNR). The authors experiment with standard video sequences as well as 300 security, surveillance, news, and speech videos. These videos total 19.15 hours of recording time. They consider both MPEG-4 and H.264 compression standards. This paper analyzes video rate adaptation techniques in computer vision applications, including Automated Video Surveillance (AVS). As in all other video streaming applications, the video streams in computer vision systems should be adapted to the dynamically changing network conditions.