Abstract:
There are many challenges facing image quality assessment (IQA) task. The greatest one which has been treated by this research is the difficulty of quantifying and evaluating distorted images quality blindly with no existence of the original (reference) image or partially from it. Choosing the appropriate features plays a significant role in measuring image quality. This study evaluates the efficiency of a set of features in quantifying image quality. The features have been gathered in spatial domain using the techniques of both rich edges and sharper regions of pristine natural images. The performance efficiency of these features examined through comparing them with both features gathered from reference and distorted images. These techniques employed to build two IQA metrics. Results clearly show the proposed pristine natural features competes reference features in assessing the distorted image quality. This proves the validity of these features in creating a robust metrics for evaluating distorted images. When testing the proposed metrics on LIVE database, experiment results show extracting features by means of rich edges is better than extracting it using sharper regions when assess the prediction monotonicity and applying the prediction accuracy evaluation. Besides they show the average outcome of the two techniques not only competes the popular full-reference peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the developed NR natural image quality evaluator (NIQE) model but also outperform them
