Confusing Image Quality Assessment: Towards Better Augmented Reality Experience

TL;DR

This paper introduces a new framework for assessing the perceptual quality of confusing, superimposed images in augmented reality, proposing new databases, subjective studies, and objective metrics to improve quality evaluation.

Contribution

It presents the concept of confusing image quality assessment (CFIQA), establishes related databases, and develops an ARIQA metric tailored for AR scenarios, advancing the evaluation of mixed real and virtual images.

Findings

CFIQA database with 900 images created for research.

Subjective and objective evaluations reveal how humans perceive confusing images.

Proposed ARIQA metric outperforms traditional IQA methods in AR contexts.

Abstract

With the development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary value of AR is to promote the fusion of digital contents and real-world environments, however, studies on how this fusion will influence the Quality of Experience (QoE) of these two components are lacking. To achieve better QoE of AR, whose two layers are influenced by each other, it is important to evaluate its perceptual quality first. In this paper, we consider AR technology as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. A more general problem is first proposed, which is evaluating the perceptual quality of superimposed images, i.e., confusing image quality assessment. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. Then a subjective quality perception study and an objective model evaluation experiment are conducted towards attaining a better understanding of how humans perceive the confusing images. An objective metric termed CFIQA is also proposed to better evaluate the confusing image quality. Moreover, an extended ARIQA study is further conducted based on the CFIQA study. We establish an ARIQA database to better simulate the real AR application scenarios, which contains 20 AR reference images, 20 background (BG) reference images, and 560 distorted images generated from AR and BG references, as well as the correspondingly collected subjective quality ratings. We also design three types of full-reference (FR) IQA metrics to study whether we should consider the visual confusion when designing corresponding IQA algorithms. An ARIQA metric is finally proposed for better evaluating the perceptual quality of AR images.