Measuring video quality degradation using face detection

Ensuring end-to-end video quality requires monitoring quality in real-time (in-service) and taking counter-measures in times of adverse network conditions. Such application-layer QoS assurance mechanisms require light-weight video quality metrics that can be implemented with low computational and communication overheads. In this paper, we propose a novel video quality metric for video conferencing-type applications that accurately reflects user opinion and is light-weight for realtime operations. Our motivation is to exploit the characteristics of the video content in such applications, i.e. few speakers with limited motion. Our metric, Simplified Perceptual Quality Region (SPQR), relies on detecting the location of a speaker's face in sent and received video frames and comparing the locations between the corresponding frames in the two streams to identify discrepancies as a sign of video quality degradation. Our experiments show that face locations can be determined in realtime by sampling few frames every second. SPQR is a reduced-reference metric that requires minimal transmission overhead between the sender and receiver through a separate channel to communicate the reduced features. In this paper, we present an empirical evaluation of the performance of SPQR using a video phone application. We first show that SPQR effectively detects video quality degradation. Second, we compare our proposed metric to two well-accepted full-reference techniques appropriate for offline analysis, namely PSNR and VQM, and show that SPQR tracks both metrics well. Finally, we show that low grade sampling yields SPQR values comparable to PSNR and VQM scores and thus enabling a light-weight implementation.