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.

通过 Compare2Score 模型，我们成功地将文本定义的比较级别与转换后的单个图像质量得分进行了有效的联系，不仅在训练过程中有效地使用了丰富的图像质量评估数据集，而且在推理过程中通过概率矩阵自适应地转换了离线图像到连续质量得分的比较级别，从而成功超越了目前最先进的图像质量评估模型。