HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment

TL;DR

HiRQA is a self-supervised, opinion-unaware image quality assessment framework that predicts quality scores using hierarchical ranking and contrastive learning, demonstrating strong generalization and real-time performance.

Contribution

It introduces a novel hierarchical ranking and contrastive alignment approach for no-reference image quality assessment that does not rely on subjective labels or pristine references.

Findings

Achieves state-of-the-art performance on synthetic and authentic benchmarks.

Effectively generalizes to real-world distortions like haze and motion blur.

Enables real-time quality assessment with a lightweight variant.

Abstract

Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder their generalization performance. We propose HiRQA, Hierarchical Ranking and Quality Alignment), a self-supervised, opinion-unaware framework that offers a hierarchical, quality-aware embedding through a combination of ranking and contrastive learning. Unlike prior approaches that depend on pristine references or auxiliary modalities at inference time, HiRQA predicts quality scores using only the input image. We introduce a novel higher-order ranking loss that supervises quality predictions through relational ordering across distortion pairs, along with an embedding distance loss that enforces consistency between feature distances and perceptual differences. A training-time contrastive alignment loss, guided by structured textual prompts, further enhances the learned representation. Trained only on synthetic distortions, HiRQA generalizes effectively to authentic degradations, as demonstrated through evaluation on various distortions such as lens flare, haze, motion blur, and low-light conditions. For real-time deployment, we introduce \textbf{HiRQA-S}, a lightweight variant with an inference time of only 3.5 ms per image. Extensive experiments across synthetic and authentic benchmarks validate HiRQA's state-of-the-art (SOTA) performance, strong generalization ability, and scalability.