Video texture and motion based modeling of rate variability-distortion (VD) curves (extended version

Abstract—We examine and model the bit rate variability-distor-tion (VD) curve of-4 Part 2 variable bit rate (VBR) video encod-ings. The VD curve has important applications for evaluating the statistical multiplexing of streaming video. We show that the con-cave VD curve shape at high compression ratios, or equivalently large quantization scales, is influenced by both the texture and the motion information. Based on this insight, we first develop a gen-eral VD curve model by analytically expressing the VD curve in terms of elementary statistics (mean, variance, covariance) of the numbers of motion and texture coding bits. In a second step we develop and validate linear and quadratic models for the elemen-tary texture and motion bit statistics, whereby the model param-eters are obtained from only two sample encodings. The texture and motion bit models are then employed in our general VD curve model. This work extends our previous work on piecewise models of the VD curve. The texture and motion based VD model obtained from two sample encodings has comparable accuracy to a piece-wise model based on three sample encodings. In addition, the tex-ture and motion based VD model provides fundamental insights into how texture and motion affect traffic variability. Index Terms—Communication systems,-4 Part 2, rate vari-ability-distortion, statistical multiplexing, variable bit rate, vide