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How To Exploit the Transferability of Learned Image Compression to Conventional Codecs

CVPR 2021 · Image generation › Image restoration · approach: Learned image compression · 20 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add surrogate-guided pre-filtering optimized for MS-SSIMConventional codecs · Kodakrate savings: over 20%Helped
Add surrogate-guided pre-filtering optimized for MS-SSIMConventional codecs · CLIC Professional validationrate savings: up to 15%Helped
Use classifier feedback to train a pre-filter for JPEGImageNet image recognition compression · ImageNetclassification performanceHelped
Add GAN-guided filtering to H265 inputH265 · Visual comparison examplesrate (bit/pixel): 0.086 bit/pixel -> 0.084 bit/pixel; 0.045 bit/pixel -> 0.042 bit/pixel; 0.190 bit/pixel -> 0.192 bit/pixelMixed
Use an MS-SSIM-trained surrogate instead of an MSE-trained surrogate (ablation)Surrogate-induced filtering · Kodakrate savingsNo clear effect
Initialize the task-aware filter with high rather than very low variance (ablation)Task-aware surrogate-induced filtering · ImageNet image recognitionclassification performanceHurt
Use the surrogate reconstruction rather than the target codec reconstruction for classifier feedback (ablation)Task-aware surrogate-induced filtering · ImageNet image recognitionclassification performanceHurt
Use a GAN loss weight low enough to allow only small image changes (ablation)GAN-guided filtering · Low-rate image compressiontexture replacementHurt

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