  We introduce a novel cross-reference image quality assessment method that
effectively fills the gap in the image assessment landscape, complementing the
array of established evaluation schemes -- ranging from full-reference metrics
like SSIM, no-reference metrics such as NIQE, to general-reference metrics
including FID, and Multi-modal-reference metrics, e.g., CLIPScore. Utilising a
neural network with the cross-attention mechanism and a unique data collection
pipeline from NVS optimisation, our method enables accurate image quality
assessment without requiring ground truth references. By comparing a query
image against multiple views of the same scene, our method addresses the
limitations of existing metrics in novel view synthesis (NVS) and similar tasks
where direct reference images are unavailable. Experimental results show that
our method is closely correlated to the full-reference metric SSIM, while not
requiring ground truth references.
