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

The Role of Stem Noise in Visual Perception and Image Quality Measurement