Abstract
Typically, in many situations, image quality assessment requires a reference or "ground truth" image to provide a quantitative measure. Popular quality measures such as the Peak Signal Noise Ratio (PSNR) or simply the Root Mean Squared Error (RMSE) are simple to calculate, but it is well known that they are not always well correlated with the perceived visual quality. Provided that a reference image is not always available, other blind quality assessment methods have been proposed to achieve a measure of the image quality assessment. In this paper, a new self-contained logarithmic measure that not requires the knowledge of a ground-truth image is introduced. This new measure is based on the use of a particular type of the high-order Rényi entropies. This method is based on measuring the anisotropy of the image through the variance of the expected value of the pixel-wise directional image entropy. Thus, a new logarithmic quality measure (LQM) is applied to a set of test images and compared with PSNR and other recently proposed quality metrics to reveal advantages and differences with them.
Published Version
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