# Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

arXiv: https://arxiv.org/abs/1907.05345 · HTML: https://mlsanity.com/paper/1907.05345/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 30 of 57)

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- [CUNSB-RFIE: Context-aware Unpaired Neural Schr"odinger Bridge in Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.10966/) (2024)
- [Image Quality-aware Diagnosis via Meta-knowledge Co-embedding](https://mlsanity.com/paper/2303.15038/) (2023)
- [PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation](https://mlsanity.com/paper/2305.07816/) (2023)
- [A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning](https://mlsanity.com/paper/2309.00885/) (2023)
- [Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.07862/) (2024)
- [RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models](https://mlsanity.com/paper/2503.03987/) (2025)
- [FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading](https://mlsanity.com/paper/2205.01676/) (2022)
- [Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement](https://mlsanity.com/paper/2312.01338/) (2023)
- [MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs](https://mlsanity.com/paper/2510.01691/) (2025)
- [Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding](https://mlsanity.com/paper/2201.11506/) (2022)
- [Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework](https://mlsanity.com/paper/2302.11795/) (2023)
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- [TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2411.01403/) (2024)
- [EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting](https://mlsanity.com/paper/2508.21550/) (2025)
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- [Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement](https://mlsanity.com/paper/2301.06943/) (2023)
- [Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement](https://mlsanity.com/paper/2303.04603/) (2023)
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- [Lightening Anything in Medical Images](https://mlsanity.com/paper/2406.10236/) (2024)
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## Abstract (arXiv)

Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., `Accept' and `Reject'). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., `Good', `Usable' and `Reject') for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.