No-reference transform-based quality assessment for imaging applications

This paper aims to introduce a new transform domain no-reference (NR) image quality assessment that predicts the perceptual quality for improving imaging systems performance. The main idea is that enhancing the contrast of diverse real-world digital images by creating more high-frequency content in the improved image than the original image. The proposed measure uses different fast orthogonal transforms, such as Fourier and Fibonacci. A generalized transform-based model of local transform-based coefficients is derived and transformed the model parameters into features used for perceptual image quality score prediction. To test the performance of the proposed algorithm, we use the well-known and publicly available database TID2008. The Pearson correlation coefficient is utilized to measure and compare the proposed quality measures performance with state-of-the-art approaches.