AIM 2024 Challenge on UHD Blind Photo Quality Assessment

TL;DR

The AIM 2024 UHD-IQA Challenge aims to advance no-reference image quality assessment for high-resolution, aesthetically superior photos by encouraging the development of efficient, high-performance models suitable for edge devices.

Contribution

This paper introduces a new UHD-IQA benchmark dataset and a challenge focused on developing efficient NR-IQA models for 4K images, emphasizing practical deployment and high-quality assessment.

Findings

Participants employ knowledge distillation and multi-scale training techniques.

Models achieve high correlation with perceptual quality ratings.

The challenge promotes scalable, edge-deployable IQA solutions.

Abstract

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.