Autoregressive Video Modeling through 2D Wavelet Statistics

We present an Autoregressive (AR) modeling method for video signal analysis based on 2D Wavelet Statistics. The video signal is assumed to be a combination of spatial feature time series that are temporally approximated by the AR model. The AR model yields a linear approximation to the temporal evolution of a stationary stochastic process. Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform sub bands, are used as the spatial features. Wavelet transform efficiently resembles the Human Visual System (HVS) characteristics and captures more suitable features, as compared to color histogram features. The AR model describes each spatial feature vector as a linear combination of the previous vectors within a reasonable time interval. Shot boundaries are well detected based on the AR prediction errors, and then at least one key frame is extracted from each shot. Experimental results confirm high accuracy of the proposed method compared to existing methods, such as [5].