Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach

TL;DR

This paper introduces a novel bag of features approach for predicting perceptual image quality on authentically distorted images, effectively handling complex real-world distortions without assuming specific types.

Contribution

The study presents a new feature-based method that captures natural scene statistics across multiple color spaces, improving quality prediction on authentic distortions compared to existing models.

Findings

Outperforms leading models on benchmark datasets

Effective in predicting quality of complex real-world distortions

Utilizes a large database of authentically distorted images

Abstract

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore they learn image features that effectively predict human visual quality judgments of inauthentic, and usually isolated (single) distortions. However, real-world images usually contain complex, composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images, in different color spaces and transform domains. We propose a bag of feature-maps approach which avoids assumptions about the type of distortion(s) contained in an image, focusing instead on capturing consistencies, or departures therefrom, of the statistics of real world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features towards improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good quality prediction power that is better than other leading models.