Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy

TL;DR

This paper introduces a novel approach combining pair comparison and ACR data to enhance quality assessment accuracy while reducing complexity, using a generic model, fusion strategy, and active sampling.

Contribution

It proposes a new methodology that integrates pair comparison with ACR data, employing a generic model, fusion strategy, and MST-based active sampling to improve quality assessment efficiency.

Findings

Achieves pair comparison accuracy with ACR-level complexity.

Outperforms existing state-of-the-art methods.

Demonstrates efficiency and robustness through extensive experiments.

Abstract

The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human observers. Different subjective assessment protocols can be used according to the objectives, which determine the discriminability and accuracy of the subjective data. Single stimulus methodology, e.g., the Absolute Category Rating (ACR) has been widely adopted due to its simplicity and efficiency. However, Pair Comparison (PC) is of significant advantage over ACR in terms of discriminability. In addition, PC avoids the influence of observers' bias regarding their understanding of the quality scale. Nevertheless, full pair comparison is much more time-consuming. In this study, we therefore 1) employ a generic model to bridge the pair comparison data and ACR data, where the variance term could be recovered and the obtained information is more complete; 2) propose a fusion strategy to boost pair comparisons by utilizing the ACR results as initialization information; 3) develop a novel active batch sampling strategy based on Minimum Spanning Tree (MST) for PC. In such a way, the proposed methodology could achieve the same accuracy of pair comparison but with the compelxity as low as ACR. Extensive experimental results demonstrate the efficiency and accuracy of the proposed approach, which outperforms the state of the art approaches.