CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing

TL;DR

The paper introduces CD2, a novel reduced-reference image quality assessment method that combines contrast distribution distances to improve error detection in image processing, achieving high performance with low data and computational costs.

Contribution

It presents a new IQA approach based on contrast distribution distances, enhancing error detection in image processing with minimal data and computational overhead.

Findings

High performance on IQA benchmarks

Low data and computational overhead

Effective error detection in image processing

Abstract

The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lacks redundancy regarding safety. We propose a novel image comparison method called the Combined Distances of Contrast Distributions (CD2) to protect against errors that arise during processing. Based on the distribution of image contrasts a new reduced-reference image quality assessment (IQA) method is introduced. By combining various distance functions excellent performance on IQA benchmarks is achieved with only a small data and computation overhead.