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