Qingpeng Xie

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.

Perceived Coding Distortion Assessment for Streaming Video

For applications involving video streaming, full decoding is usually not acceptable for quality assessment. To address the inherent challenges, an efficient method for coding distortion assessment is proposed in this paper. Building on empirical analysis, the proposed method employs a linear model to assess the coding distortion using the quantization scale. Furthermore, the characteristics of the human visual system are exploited by taking into account the spatial and temporal masking. To estimate the required spatial and temporal complexities in absence of sufficient information, a rate-distortion model is theoretically derived to formulate their relationship with the coding bit-rate. Extensive experimental results have demonstrated the effectiveness of the proposed method for quality assessment with respect to perceived coding distortion.