Visual Quality Improvement of Digital Video by Stabilization Using Adaptive CMAC Filtering

Abstract: A Digital Video Stabilization (DVS) system removes the unwanted shaking in the videos acquired by hand-held cameras and preserves the panning. In this thesis work, a digital video stabilization model is proposed based upon adaptive cerebellar model articulation controller (CMAC) filtering. A CMAC is a manifestation of the associative memory learning structure present in the cerebellum of human being. Adaptive CMAC filtering has favorable properties of small size, good generalization, rapid learning and dynamic response. So, it is more suitable for high-speed signal processing applications. The adaptive CMAC is used to adjust the coefficients of IIR filter employed in the proposed model. The training of CMAC is based upon a fuzzy rule. The efficiency of the proposed adaptive CMAC filtering has been validated by evaluating it on a set of test videos sequences. We have done performance comparison of CMAC algorithm with already existing algorithms such Kalman filter, Modified proportional integrated (MPI) controller and adaptive fuzzy filtering in terms of smooth index (SI), peak signal to noise ratio (PSNR), mean square error (MSE) and point spread function (PSF).