  This paper considers reference free quality assessment of distorted and noisy
images. Specifically, it considers the first and second order statistics of
stem noise that can be evaluated given any image. In the research field of
Image quality Assessment (IQA), the stem noise is defined as the input of an
Auto Regressive (AR) process, from which a low-energy and de-correlated version
of the image can be recovered. To estimate the AR model parameters and
associated stem noise energy, the Yule-walker equations are used such that the
accompanying Auto Correlation Function (ACF) coefficients can be treated as
model parameters for image reconstruction. To characterize systematic signal
dependent and signal independent distortions, the mean and variance of stem
noise can be evaluated over the image. Crucially, this paper shows that these
statistics have a predictive validity in relation to human ratings of image
quality. Furthermore, under certain kinds of image distortion, stem noise
statistics show very significant correlations with established measures of
image quality.

Image and Video Processing;Computer Vision and Pattern Recognition