Video Texture and Motion Based Modeling of Rate Variability-Distortion (VD) Curves

We examine and model the bit rate variability-distortion (VD) curve of -4 part 2 variable bit rate (VBR) video encodings. The VD curve has important applications for evaluating the statistical multiplexing of streaming video. We show that the concave 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 general 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 elementary texture and motion bit statistics, whereby the model parameters 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 piecewise model based on three sample encodings. In addition, the texture and motion based VD model provides fundamental insights into how texture and motion affect traffic variability.