Predictive modeling of the rate-distortion of scalable video sequences based on complexity

Rate-Distortion (R-D) models are applied in video processing techniques to predict the bit rate required for the transmission of video sequences in the networks. Video coding techniques are utilized to smartly divide the bandwidth in the communication networks. This research presents predictive modeling of the Rate-Distortion of scalable video sequences based on complexity. Base-Layer (BL) rate and Enhancement-Layer (EL) rate models are developed alongside R-D models to get more precise rate predictions. Videos will be classified based on low and high complexity. This classification is achieved by the Spatial-Index (SI) and Temporal-Index (TI) of video sequences Group of Pictures (GOP) wise. By using the role of SI and TI, an algorithm is developed to predict the rate required by video sequences of low and high complexity with the given distortion and vice versa. The experimental results have shown the productive comparison of presented R-D models and existing R-D models.