IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models

• URL: http://arxiv.org/abs/2412.01794v1
• Date: Mon, 02 Dec 2024 18:40:19 GMT
• Title: IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models
• Authors: Khaled Abud, Sergey Lavrushkin, Alexey Kirillov, Dmitriy Vatolin,
• Abstract summary: We introduce IQA-Adapter, a novel architecture that conditions generation on target quality levels by learning the relationship between images and quality scores.<n>IQA-Adapter achieves up to a 10% improvement across multiple objective metrics, as confirmed by a subjective study.<n>Our quality-aware methods also provide insights into the adversarial robustness of IQA models.
• Score: 0.5356944479760104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based models have recently transformed conditional image generation, achieving unprecedented fidelity in generating photorealistic and semantically accurate images. However, consistently generating high-quality images remains challenging, partly due to the lack of mechanisms for conditioning outputs on perceptual quality. In this work, we propose methods to integrate image quality assessment (IQA) models into diffusion-based generators, enabling quality-aware image generation. First, we experiment with gradient-based guidance to optimize image quality directly and show this approach has limited generalizability. To address this, we introduce IQA-Adapter, a novel architecture that conditions generation on target quality levels by learning the relationship between images and quality scores. When conditioned on high target quality, IQA-Adapter shifts the distribution of generated images towards a higher-quality subdomain. This approach achieves up to a 10% improvement across multiple objective metrics, as confirmed by a subjective study, while preserving generative diversity and content. Additionally, IQA-Adapter can be used inversely as a degradation model, generating progressively more distorted images when conditioned on lower quality scores. Our quality-aware methods also provide insights into the adversarial robustness of IQA models, underscoring the potential of quality conditioning in generative modeling and the importance of robust IQA methods.

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