ReLIQS, a CLIP-based multiscale patch model with learned importance-guided patch selection, achieves SOTA or near-SOTA no-reference image quality scores across authentic, synthetic, AIGC, and ultra-high-resolution benchmarks while preserving original-resolution detail and keeping compute adjustable.
Nima: Neural image assessment.IEEE Transactions on Image Processing, 27(8): 3998–4011, 2018
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Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency
ReLIQS, a CLIP-based multiscale patch model with learned importance-guided patch selection, achieves SOTA or near-SOTA no-reference image quality scores across authentic, synthetic, AIGC, and ultra-high-resolution benchmarks while preserving original-resolution detail and keeping compute adjustable.