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Abstract
It is commonly known that the mean square error (MSE) does not accurately reflect the subjective image quality for most video enhancement tasks. Among the various image quality metrics, structural similarity (SSIM) metric provides remarkably good prediction of the subjective scores. In this paper, a new registration method based on contribution of structural similarity measurement to the well known Lucas–Kanade (LK) algorithm has been proposed. The core of the proposed method is contributing the SSIM in the sum of squared difference of images along with the Levenberg–Marquardt optimisation approach in LK algorithm. Mathematical derivation of the proposed method, based on the unified framework of Baker et al., is given. The proposed registration algorithm is applied to a video enhancement successfully. Various objective and subjective comparisons show the superior performance of the proposed method.
The authors are indebted to anonymous referees for valuable comments. We would also like to thank Dr Vandewalle VandewalleCitation25 for his SR package and Dr D. Lowe for his SIFT key point program (http://www.cs.ubc.ca/lowe/keypoints/). We also thank to Dr Peter Kovesi (http://www.csse.uwa.edu.au/pk/research/matlabfns/), Dr Simon Baker and his co-workersCitation6 for providing many useful MATLAB functions and Dr Gh. Mohajeri for ‘Tokyo’ sequence.