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

Srinath, S and Mitra, S and Rao, S and Soundararajan, R (2024) Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment. In: UNSPECIFIED, pp. 22-31.

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Official URL: https://doi.org/10.1109/WACV57701.2024.00010

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. © 2024 IEEE.

Item Type: Conference Paper
Publication: Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to authors.
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 27 May 2024 05:35
Last Modified: 27 May 2024 05:36
URI: https://eprints.iisc.ac.in/id/eprint/85039

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