Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency

2026-08-03

AI Summary

Existing no-reference image quality assessment methods suffer from critical limitations in multi-resolution scenarios, including loss of essential quality cues, poor cross-resolution generalization, difficulty in jointly training on heterogeneous data, and high computational overhead. This work proposes ReLIQS, a novel model that achieves resolution-agnostic quality prediction for the first time by integrating multi-scale patch sampling, a CLIP vision backbone, a perceptual importance estimator, and a latent quality-axis aggregation module. ReLIQS preserves original-resolution quality signals while enabling robust cross-resolution generalization and joint training across heterogeneous MOS scales, further enhanced by a quality-aware saliency mechanism that dynamically selects informative regions. Experiments demonstrate that ReLIQS consistently outperforms CNN-, CLIP-, and MLLM-based baselines on diverse benchmarks encompassing real-world, synthetic, and AIGC-generated images, achieving superior performance at comparable or lower computational cost.

Abstract

No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they either discard critical quality cues via aggressive resizing, fail to generalize across resolutions, cannot be jointly trained on heterogeneous IQA datasets with mismatched MOS scales, or require prohibitive computation. We present \textbf{ReLIQS}, a model for \textbf{Re}solution-agnostic \textbf{L}earning for \textbf{I}mage \textbf{Q}uality with \textbf{S}aliency, which is resolution-agnostic, preserves original-resolution quality cues, learns from multiple subjective studies, and remains computationally efficient and budget-adaptive. ReLIQS is a CLIP-based multiscale patch-driven architecture that learns both \emph{where to look} and \emph{how to judge} quality. Fixed-size patches are sampled across multiple resolutions, including the original resolution, and encoded with a CLIP vision backbone. A lightweight Perceptual Importance Estimator then predicts IQA-specific importance maps to select a small set of informative patches, and a Latent Quality Axis Module aggregates their embeddings into a single image-level score. Across authentic, synthetic, and AIGC benchmarks spanning diverse resolutions and distortions, ReLIQS generalizes better than strong CNN-, CLIP-, and MLLM-based baselines with matching or reduced computational cost.

Problem

• No-reference Image Quality Assessment
• Resolution-agnostic
• Quality-aware Saliency
• Heterogeneous IQA Datasets
• Computational Efficiency

Innovation

• resolution-agnostic
• quality-aware saliency
• no-reference image quality assessment
• multiscale patch selection
• CLIP-based architecture