Abstract
In this thesis, a novel true motion estimation method and its application on noise reduction for video sequences are proposed. This estimation method is based on the mixture of 3DRS and block-based searching, along with a confidence model of true motion. It can deal with various kinds of true motion (stationary, moving, camera zooming, panning, etc.) in versatile video sequences. Because of the inherent nature of distortion in projecting 3D motion to a series of 2D motion frames, we deliberately evaluate the reliability of each estimated vector so as to ascertain its fidelity. Once the reliability (or called the true motion confidence) secured, the motion vector having the better confidence level will be retained and propagated to the neighboring blocks in the recursive search procedure. Moreover, in this thesis, a compensation-based noise reduction algorithm for video sequences is proposed, which applies the above technique and other capabilities as well to demonstrate the fidelity performance. Experimental results show that this noise reduction algorithm cleanup the noises to an acceptable level while preserving the details in texture.