Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment

• URL: http://arxiv.org/abs/2312.04838v1
• Date: Fri, 8 Dec 2023 05:24:21 GMT
• Title: Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment
• Authors: Suhas Srinath, Shankhanil Mitra, Shika Rao and Rajiv Soundararajan
• Abstract summary: A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations. We enable the learning of low-level quality features to distortion types by introducing a novel quality-aware contrastive loss. We design zero-shot quality predictions from both pathways in a completely blind setting.
• Score: 7.291687946822539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications. A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations to train models for a target IQA application. To mitigate this requirement, there is a need for unsupervised learning of generalizable quality representations that capture diverse distortions. We enable the learning of low-level quality features agnostic to distortion types by introducing a novel quality-aware contrastive loss. Further, we leverage the generalizability of vision-language models by fine-tuning one such model to extract high-level image quality information through relevant text prompts. The two sets of features are combined to effectively predict quality by training a simple regressor with very few samples on a target dataset. Additionally, we design zero-shot quality predictions from both pathways in a completely blind setting. Our experiments on diverse datasets encompassing various distortions show the generalizability of the features and their superior performance in the data-efficient and zero-shot settings. Code will be made available at https://github.com/suhas-srinath/GRepQ.

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