Motion tuned spatio-temporal quality assessment of natural videos
IEEE Transactions on Image Processing
Multichannel image restoration based on optimization of the structural similarity index
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Automatic prediction of perceptual quality of multimedia signals--a survey
Multimedia Tools and Applications
Visual quality assessment algorithms: what does the future hold?
Multimedia Tools and Applications
Missing texture reconstruction method based on perceptually optimized algorithm
EURASIP Journal on Advances in Signal Processing
Structural similarity-based approximation of signals and images using orthogonal bases
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Perceptually optimized blind repair of natural images
Image Communication
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We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm.