Content-Adaptive Packet-Layer Model for Quality Assessment of Networked Video Services

Abstract

Packet-layer models are designed to use only the information provided by packet headers for real-time and non-intrusive quality monitoring of networked video services. This paper proposes a content-adaptive packet-layer (CAPL) model for networked video quality assessment. Considering the fact that the quality degradation of a networked video significantly relies on the temporal as well as the spatial characteristics of the video content, temporal complexity is incorporated in the proposed model. Due to very limited information directly available from packet headers, a simple and adaptive method for frame type detection is adopted in the CAPL model. The temporal complexity is estimated using the ratio of the number of bits for coding P and I frames. The estimated temporal complexity and frame type are incorporated in the CAPL model together with the information about the number of bits and positions of lost packets to obtain the quality estimate for each frame, by evaluating the distortions induced by both compression and packet loss. A two-level temporal pooling is employed to obtain the video quality given the frame quality. Using content related information, the proposed model is able to adapt to different video contents. Experimental results show that the CAPL model significantly outperforms the G.1070 model and the DT model in terms of widely used performance criteria, including the Root-Mean-Squared Error (RMSE), the Pearson Correlation Coefficient (PCC), the Spearman Rank Order Correlation Coefficient (SCC), and the Outlier Ratio (OR).