A Novel Computationally Efficient Motion Compensation Method Based on Pixel by Pixel Prediction

Abstract: In this paper, we are proposing a simple lossless prediction based coding method for videos. Our algorithm works on 3 modes of operations and selection of the mode is done on a pixel-by-pixel basis. Selection of the mode is based on intensity variation of pixels in temporal direction. If there is a large intensity value variation, intraframe prediction mode is chosen. Otherwise interframe prediction mode is activated. Intraframe prediction uses Gradient Adaptive Predictor [5] whereas Interframe prediction switches between two algorithms. In interframe case, one prediction mode takes care of local characteristics of pixels of current and motion compensated frames while the other incorporates global characteristics. The proposed method is computationally very simple and results into better performance as compared to competitive but complex methods reported in literature.

Abstract: This paper aims to propose a noble method to estimate the auto-regressive(AR) coefficients used by least-square(LS) based predictors. Estimation of this LS based predictors is computationally most complex process. This process requires a covariance matrix comprised of chosen causal pixels and also the inverse elements of the same matrix. Computational requirements of this process depends on the number of pixels for which the predictor is trained and also on the order of the predictor. Due to this high complexity, the predictor is not used practically although it provides a high compression ratio. Thus, an alternative algorithm, popularly known as LOPT-3D, was proposed in literature. However, the number of pixels required for the estimation of AR parameters are still large, and thereby, making it impracticable for real-time implementations. The proposed method overcomes this limitation by effectively making use of previously estimated AR parameters.