Perceptual Image Quality Assessment through Spectral Analysis of Error Representations

TL;DR

This paper introduces SUMMER, a spectral analysis-based image quality assessment method that effectively models both spatial and color distortions, outperforming existing algorithms across multiple databases and distortion types.

Contribution

The paper proposes SUMMER, a novel spectral analysis approach for image quality assessment that incorporates multi-scale and multi-channel error representations, addressing limitations of previous spectral methods.

Findings

SUMMER outperforms most existing algorithms in quality prediction accuracy.

Spectral analysis of error signals effectively captures perceptual image distortions.

The method is validated across diverse distortion types and databases.

Abstract

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