  While recent advancements in large multimodal models (LMMs) have
significantly improved their abilities in image quality assessment (IQA)
relying on absolute quality rating, how to transfer reliable relative quality
comparison outputs to continuous perceptual quality scores remains largely
unexplored. To address this gap, we introduce Compare2Score-an all-around
LMM-based no-reference IQA (NR-IQA) model, which is capable of producing
qualitatively comparative responses and effectively translating these discrete
comparative levels into a continuous quality score. Specifically, during
training, we present to generate scaled-up comparative instructions by
comparing images from the same IQA dataset, allowing for more flexible
integration of diverse IQA datasets. Utilizing the established large-scale
training corpus, we develop a human-like visual quality comparator. During
inference, moving beyond binary choices, we propose a soft comparison method
that calculates the likelihood of the test image being preferred over multiple
predefined anchor images. The quality score is further optimized by maximum a
posteriori estimation with the resulting probability matrix. Extensive
experiments on nine IQA datasets validate that the Compare2Score effectively
bridges text-defined comparative levels during training with converted single
image quality score for inference, surpassing state-of-the-art IQA models
across diverse scenarios. Moreover, we verify that the probability-matrix-based
inference conversion not only improves the rating accuracy of Compare2Score but
also zero-shot general-purpose LMMs, suggesting its intrinsic effectiveness.
