AI RESEARCH
ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
arXiv CS.CV
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ArXi:2605.09479v1 Announce Type: cross 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.