Boosting the No-Reference Image Quality Assessment via Low-Quality Pseudo References

No-reference image quality assessment (NR-IQA) aims to predict perceptual image quality without access to pristine references, which remains challenging due to diverse and complex distortions. Recent pseudo-reference-based methods attempt to mitigate this challenge but often rely on highfidelity pseudo-reference reconstruction. In contrast, this work shows that improving NR-IQA performance does not depend on reconstruction quality, but on effective representation learning, feature alignment, and deviation modeling between distorted images and pseudo references. To this end, we propose a novel NR-IQA framework that leverages low-quality pseudo references generated by a masked autoencoder with a lightweight decoder. Rather than pursuing detailed reconstruction, the pseudo reference is used to facilitate representation-level deviation modeling in a shared latent space via a cross-attention-based mechanism. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art NR-IQA approaches while maintaining modest computational complexity. Our source code will be available at: https://github.com/jianjin008/L-IQA.