TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets

TL;DR

TRIQA introduces a contrastive triplet-based learning approach for no-reference image quality assessment, effectively utilizing limited data to predict perceptual quality aligned with human judgments.

Contribution

The paper presents a novel contrastive triplet-based training method for NR-IQA that requires fewer samples and generalizes well across datasets.

Findings

Effective with limited data

Strong generalization across datasets

Outperforms existing methods

Abstract

Image Quality Assessment (IQA) models aim to predict perceptual image quality in alignment with human judgments. No-Reference (NR) IQA remains particularly challenging due to the absence of a reference image. While deep learning has significantly advanced this field, a major hurdle in developing NR-IQA models is the limited availability of subjectively labeled data. Most existing deep learning-based NR-IQA approaches rely on pre-training on large-scale datasets before fine-tuning for IQA tasks. To further advance progress in this area, we propose a novel approach that constructs a custom dataset using a limited number of reference content images and introduces a no-reference IQA model that incorporates both content and quality features for perceptual quality prediction. Specifically, we train a quality-aware model using contrastive triplet-based learning, enabling efficient training with fewer samples while achieving strong generalization performance across publicly available datasets. Our repository is available at https://github.com/rajeshsureddi/triqa.