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Group MAD Competition - A New Methodology to Compare Objective Image Quality Models

CVPR 2016 · Image generation › Autoregressive and latent generation · approach: Learning-based image quality assessment · 98 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use multi-scale SSIM instead of SSIMSSIM · gMAD competition on the new IQA databaseglobal rankingHelped
Use FR-IQA models rather than NR-IQA modelsIQA models · gMAD competition on the new IQA databaseglobal rankingHelped
Use perception- and distortion-relevant natural scene statistics featuresNR-IQA models · gMAD competition on the new IQA databaseglobal rankingHelped
Use LPSI's one-dimensional feature space without MOS training instead of sophisticated machine-learning approachesNR-IQA models · gMAD competition on the new IQA databaseglobal rankingHelped
Train IQA models on existing publicly available databasesMachine-learning-based IQA models · gMAD competition on the new IQA databaseglobal rankingHurt
Use training-based IQA models rather than non-training-based modelsIQA models · Rational consistency test on the new IQA databaseKRCC consistencyHurt
Use FR-IQA models rather than NR-IQA modelsIQA models · Rational consistency test on the new IQA databaseKRCC consistencyHelped
Evaluate models with gMAD-selected image pairs instead of relying only on pre-defined subject-rated samplesIQA model comparison · New IQA databasesubjective discriminationHelped

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