# Confusing Image Quality Assessment: Towards Better Augmented Reality Experience

arXiv: https://arxiv.org/abs/2204.04900 · HTML: https://mlsanity.com/paper/2204.04900/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 93 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Unified Image and Video Saliency Modeling](https://mlsanity.com/paper/2003.05477/) (2020)
- [No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness](https://mlsanity.com/paper/2102.11393/) (2021)
- [A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment](https://mlsanity.com/paper/1705.05126/) (2017)
- [Subjective and Objective Visual Quality Assessment of Textured 3D Meshes](https://mlsanity.com/paper/2102.03982/) (2021)

## Built on by (most cited first, 30 of 32)

- [AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence](https://mlsanity.com/paper/2307.00211/) (2023)
- [FineVQ: Fine-Grained User Generated Content Video Quality Assessment](https://mlsanity.com/paper/2412.19238/) (2024)
- [AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM](https://mlsanity.com/paper/2411.17221/) (2024)
- [Saliency in Augmented Reality](https://mlsanity.com/paper/2204.08308/) (2022)
- [AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images](https://mlsanity.com/paper/2404.01024/) (2024)
- [ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality](https://mlsanity.com/paper/2501.12553/) (2025)
- [PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images](https://mlsanity.com/paper/2311.15556/) (2023)
- [Quality Assessment for AI Generated Images with Instruction Tuning](https://mlsanity.com/paper/2405.07346/) (2024)
- [Perceptual Quality Assessment of Omnidirectional Audio-visual Signals](https://mlsanity.com/paper/2307.10813/) (2023)
- [MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration](https://mlsanity.com/paper/2510.08508/) (2025)
- [Omni2: Unifying Omnidirectional Image Generation and Editing in an Omni Model](https://mlsanity.com/paper/2504.11379/) (2025)
- [A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality](https://mlsanity.com/paper/2508.09185/) (2025)
- [Audio-visual Saliency for Omnidirectional Videos](https://mlsanity.com/paper/2311.05190/) (2023)
- [How is Visual Attention Influenced by Text Guidance? Database and Model](https://mlsanity.com/paper/2404.07537/) (2024)
- [ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images](https://mlsanity.com/paper/2407.21363/) (2024)
- [HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment](https://mlsanity.com/paper/2501.01116/) (2025)
- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [RGC-VQA: An Exploration Database for Robotic-Generated Video Quality Assessment](https://mlsanity.com/paper/2506.23852/) (2025)
- [Evaluating Image Editing with LLMs: A Comprehensive Benchmark and Intermediate-Layer Probing Approach](https://mlsanity.com/paper/2603.19775/) (2026)
- [Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition](https://mlsanity.com/paper/2604.05523/) (2026)
- [MVBIND: Self-Supervised Music Recommendation For Videos Via Embedding Space Binding](https://mlsanity.com/paper/2405.09286/) (2024)
- [Quality-guided Skin Tone Enhancement for Portrait Photography](https://mlsanity.com/paper/2406.15848/) (2024)
- [ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos](https://mlsanity.com/paper/2412.20423/) (2024)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)
- [Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2412.06003/) (2024)
- [Immersive Multimedia Communication: State-of-the-Art on eXtended Reality Streaming](https://mlsanity.com/paper/2506.10004/) (2025)
- [Robust Mesh Saliency Ground Truth Acquisition in VR via View Cone Sampling and Manifold Diffusion](https://mlsanity.com/paper/2601.02721/) (2026)
- [Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.08311/) (2026)
- [Preference-Guided Debiasing for No-Reference Enhancement Image Quality Assessment](https://mlsanity.com/paper/2603.20086/) (2026)
- [ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality](https://mlsanity.com/paper/2604.03112/) (2026)

## Abstract (arXiv)

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.