ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information for downstream models. We formulate machine-oriented quality as a latent machine utility and approximate it through pairwise predictive-consistency comparisons. To this end, we construct PCMP, a dataset of PSNR-matched distortion pairs labeled by consistency votes from multiple pretrained models. We further propose ML-CLIPSim, a differentiable quality metric built on a frozen CLIP visual encoder, which aggregates intermediate patch-token similarities and global image embeddings. Experiments on machine-preference benchmarks, human-IQA dataset...
| Authors | Feng Ding, Haisheng Fu, Jie Liang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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