Learning Generalizable Perceptual Representations for Data-Efficient
  No-Reference Image Quality Assessment
Suhas Srinath Shankhanil Mitra Shika Rao Rajiv Soundararajan
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.