AIM 2024 Challenge on UHD Blind Photo Quality Assessment
<|reference_start|>AIM 2024 Challenge on UHD Blind Photo Quality Assessment: We introduce the AIM 2024 UHD-IQA Challenge, a competition to advance the No-Reference Image Quality Assessment (NR-IQA) task for modern, high-resolution photos. The challenge is based on the recently released UHD-IQA Benchmark Database, which comprises 6,073 UHD-1 (4K) images annotated with perceptual quality ratings from expert raters. Unlike previous NR-IQA datasets, UHD-IQA focuses on highly aesthetic photos of superior technical quality, reflecting the ever-increasing standards of digital photography. This challenge aims to develop efficient and effective NR-IQA models. Participants are tasked with creating novel architectures and training strategies to achieve high predictive performance on UHD-1 images within a computational budget of 50G MACs. This enables model deployment on edge devices and scalable processing of extensive image collections. Winners are determined based on a combination of performance metrics, including correlation measures (SRCC, PLCC, KRCC), absolute error metrics (MAE, RMSE), and computational efficiency (G MACs). To excel in this challenge, participants leverage techniques like knowledge distillation, low-precision inference, and multi-scale training. By pushing the boundaries of NR-IQA for high-resolution photos, the UHD-IQA Challenge aims to stimulate the development of practical models that can keep pace with the rapidly evolving landscape of digital photography. The innovative solutions emerging from this competition will have implications for various applications, from photo curation and enhancement to image compression.<|reference_end|>
@article{hosu2024aim,
    title={AIM 2024 Challenge on UHD Blind Photo Quality Assessment},
    author={Vlad Hosu and Marcos V. Conde and Lorenzo Agnolucci and Nabajeet
  Barman and Saman Zadtootaghaj and Radu Timofte},
    journal={arXiv preprint arXiv:2409.16271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16271},
    primaryClass={cs.CV}
}