LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model
This addresses the need for efficient image quality assessment on mobile devices, though it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of deploying No-Reference Image Quality Assessment (NR-IQA) models on resource-constrained devices by proposing a lightweight model that achieves state-of-the-art performance on ECCV AIM UHD-IQA datasets and is 5.7 times faster than the fastest existing 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.