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In this paper, we analyze the statistics of error signals to assess theperceived quality of images. Specifically, we focus on the magnitude spectrumof error images obtained from the difference of reference and distorted images.Analyzing spectral statistics over grayscale images partially modelsinterference in spatial harmonic distortion exhibited by the visual system butit overlooks color information, selective and hierarchical nature of visualsystem. To overcome these shortcomings, we introduce an image qualityassessment algorithm based on the Spectral Understanding of Multi-scale andMulti-channel Error Representations, denoted as SUMMER. We validate the qualityassessment performance over 3 databases with around 30 distortion types. Thesedistortion types are grouped into 7 main categories as compression artifact,image noise, color artifact, communication error, blur, global and localdistortions. In total, we benchmark the performance of 17 algorithms along withthe proposed algorithm using 5 performance metrics that measure linearity,monotonicity, accuracy, and consistency. In addition to experiments withstandard performance metrics, we analyze the distribution of objective andsubjective scores with histogram difference metrics and scatter plots.Moreover, we analyze the classification performance of quality assessmentalgorithms along with their statistical significance tests. Based on ourexperiments, SUMMER significantly outperforms majority of the compared methodsin all benchmark categories

Perceptual Image Quality Assessment through Spectral Analysis of Error Representations