Multiple distortion pooling image quality assessment
Prateek Gupta, Neha Tripathi, Vikrant Bhateja · International Journal of Convergence Computing · 2013
This paper presents a quality assessment method which pools some basic image quality assessment parameters and empirically combines them in such a manner to evaluate across different distortion types. The proposed quality metric (Q) is formulated by modelling an image distortion as a combined effect of structural distortion, contrast distortion and edge distortion, which may occur on account of deteriorations due to noise contamination, contrast manipulations, blurring, rotation or compression. The values of the correlation coefficient prove that this metric provides an accurate estimation for the above mentioned distortions in comparison to mean squared error (MSE) and structural similarity measure (SSIM). Results of subjective evaluation also validate the efficiency of the proposed quality assessment method and its ability to quantify the effect of different distortions under a single quality metric.