Content-adaptive bitstream-layer model for coding distortion assessment of H.264/AVC networked video

• Published in: Journal of visual communication and image representation 2014-07, Vol.25 (5), p.1199-1208
• Main Authors: Su, Honglei, Yang, Fuzheng
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Assessments; Bitstream-layer model; Coding; Coding distortion; Coding, codes; Complexity; Computer science; control theory; systems; Correlation coefficients; Data processing. List processing. Character string processing; Distortion; Exact sciences and technology; H.264/AVC; Information, signal and communications theory; Mathematical models; Memory organisation. Data processing; Networked video; No reference; Pattern recognition. Digital image processing. Computational geometry; Quadratic programming; Signal and communications theory; Software; Spatial complexity; Telecommunications and information theory; Temporal complexity; Temporal logic; Video quality assessment
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2014.04.004

•Coding distortion of H.264/AVC networked video depends not only on QP but also on spatial and temporal complexities.•Spatial complexity can be estimated using quantization parameter and quantized coefficient.•Temporal complexity can be obtained using the weighted MV.•The proposed model is superior to the P.1202.1 model and the other two state-of-the-art models. Bitstream-layer models are designed to use the information extracted from both packet headers and payload for real-time and non-intrusive quality monitoring of networked video. This paper proposes a content-adaptive bitstream-layer (CABL) model for coding distortion assessment of H.264/AVC networked video. Firstly, the fundamental relationship between perceived coding distortion and quantization parameter (QP) is established. Then, considering the fact that the perceived coding distortion of a networked video significantly relies on both the spatial and temporal characteristics of video content, spatial and temporal complexities are incorporated in the proposed model. Assuming that the residuals before Discrete Cosine Transform (DCT) keep to the Laplace distribution, the scale parameters of the Laplace distribution are estimated utilizing QP and quantized coefficients on the basis of the Parseval theorem firstly. Then the spatial complexity is evaluated using QP and the scale parameters. Meanwhile, the temporal complexity is obtained using the weighted motion vectors (MV) considering the variations in temporal masking extent for high motion regions and low motion regions, respectively. Both the two characteristics of video content are extracted from the compressed bitstream without resorting to a complete decoding. Using content related information, the proposed model is able to adapt to different video contents. Experimental results show that the overall performance of CABL model significantly outperforms that of the P.1202.1 model and other coding distortion assessment models in terms of widely used performance criteria, including the Pearson Correlation Coefficient (PCC), the Spearman Rank Order Correlation Coefficient (SROCC), the Root-Mean-Squared Error (RMSE) and the Outlier Ratio (OR).