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Content-Variant Reference Image Quality Assessment via Knowledge Distillation

AAAI 2022 · Image generation › Autoregressive and latent generation · approach: Learning-based image quality assessment · 47 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add non-aligned reference images without distillation (ablation)NR-student baseline · TID2013 and KonIQ-10KSRCC: 0.631 -> 0.679; 0.317 -> 0.352Helped
add knowledge distillation from the FR-teacher (ablation)NAR-student · TID2013 and KonIQ-10KSRCC: 0.679 -> 0.691; 0.352 -> 0.416Helped
add knowledge distillation from the FR-teacher (ablation)NAR-student · KonIQ-10K, across different NAR referencesSRCC standard deviation: 0.072 -> 0.004Helped
use pixel-aligned instead of content-variant reference images (ablation)NAR-student · LIVE, CSIQ, and TID2013SRCC: 0.913 -> 0.958; 0.829 -> 0.937; 0.691 -> 0.846Helped
use content-similar instead of content-variant NAR references (ablation)NAR-student · LIVE, CSIQ, and TID2013SRCC: 0.913 -> 0.931; 0.829 -> 0.862; 0.691 -> 0.720Helped
use higher-quality HQ reference images (ablation)NAR-student · TID2013 and KonIQ-10KSRCC: 0.665 to 0.691; 0.392 to 0.416Helped
increase the number and size of input patches (ablation)CVRKD-IQA FR-teacher and NAR-student · TID2013SRCCHelped
use content-variant non-aligned references with FR-teacher distillationCVRKD-IQA · LIVE, CSIQ, TID2013, and KonIQ-10KSRCC: 0.913; 0.829; 0.691; 0.416Helped

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