Image Quality Assessment for Defocused Blur Images
Fatin E. M. Al-Obaidi · American Journal of Signal Processing · 2015
Defocus blur is considered as an important visual cue for image quality assessment which the latter plays an effective role in various image processing applications. Image quality assessment related to image similarity in which quality is based on the difference between the degraded image and the original one. Among the well-known algorithms for measuring image quality assessment, Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), Structural Similarity Index Metric (SSIM), and structural Dissimilarity metric (DSSIM) have been used. An attempt to improve the quality of such blur has been executed which the latter treated by six different mechanisms. Results of the objective quality assessment showed the role of its parameters in providing mechanisms' quality through their applications. Among the different used mechanisms, the conversion to grayscale mechanism produces a powerful result upon the defocused blur images. Besides, results also show the highest efficiency for the objective quality assessment which has been used here to discriminate the best and worst used mechanisms upon such blur images.