GVCC achieves the lowest LPIPS on UVG at bitrates down to 0.003 bpp by encoding stochastic innovations in a marginal-preserving stochastic process derived from a pretrained rectified-flow video model, with 65% LPIPS reduction over DCVC-RT.
Generative Adversarial Networks for Extreme Learned Image Compression
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
A deep convolutional autoencoder compression framework jointly optimized with face recognition achieves higher verification accuracy on LFW images than JPEG2000 or JPEG.
A practical learned image codec delivers 2.3-3x bitrate savings over AV1/VVC and 20-40% over prior learned codecs while encoding 12MP images in 230ms on iPhone.
Learned image and video compression via autoencoders with spatial-temporal energy compaction penalties outperforms standards on MS-SSIM and visual quality.
A learned image compression system using deep residual learning and sub-pixel convolution reaches 0.972 MS-SSIM at 0.15 bits per pixel in the CLIC validation phase.
citing papers explorer
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GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow
GVCC achieves the lowest LPIPS on UVG at bitrates down to 0.003 bpp by encoding stochastic innovations in a marginal-preserving stochastic process derived from a pretrained rectified-flow video model, with 65% LPIPS reduction over DCVC-RT.
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A Deep Image Compression Framework for Face Recognition
A deep convolutional autoencoder compression framework jointly optimized with face recognition achieves higher verification accuracy on LFW images than JPEG2000 or JPEG.
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What Matters in Practical Learned Image Compression
A practical learned image codec delivers 2.3-3x bitrate savings over AV1/VVC and 20-40% over prior learned codecs while encoding 12MP images in 230ms on iPhone.
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Learning Image and Video Compression through Spatial-Temporal Energy Compaction
Learned image and video compression via autoencoders with spatial-temporal energy compaction penalties outperforms standards on MS-SSIM and visual quality.
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Deep Residual Learning for Image Compression
A learned image compression system using deep residual learning and sub-pixel convolution reaches 0.972 MS-SSIM at 0.15 bits per pixel in the CLIC validation phase.