LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model
<|reference_start|>LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model: Recent advancements in the field of No-Reference Image Quality Assessment (NR-IQA) using deep learning techniques demonstrate high performance across multiple open-source datasets. However, such models are typically very large and complex making them not so suitable for real-world deployment, especially on resource- and battery-constrained mobile devices. To address this limitation, we propose a compact, lightweight NR-IQA model that achieves state-of-the-art (SOTA) performance on ECCV AIM UHD-IQA challenge validation and test datasets while being also nearly 5.7 times faster than the fastest SOTA model. Our model features a dual-branch architecture, with each branch separately trained on synthetically and authentically distorted images which enhances the model's generalizability across different distortion types. To improve robustness under diverse real-world visual conditions, we additionally incorporate multiple color spaces during the training process. We also demonstrate the higher accuracy of recently proposed Kolmogorov-Arnold Networks (KANs) for final quality regression as compared to the conventional Multi-Layer Perceptrons (MLPs). Our evaluation considering various open-source datasets highlights the practical, high-accuracy, and robust performance of our proposed lightweight model. Code: https://github.com/nasimjamshidi/LAR-IQA.<|reference_end|>
@article{avanaki2024lar-iqa:,
    title={LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality
  Assessment Model},
    author={Nasim Jamshidi Avanaki, Abhijay Ghildyal, Nabajeet Barman and Saman
  Zadtootaghaj},
    journal={arXiv preprint arXiv:2408.17057},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17057},
    primaryClass={cs.CV cs.MM eess.IV}
}