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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Variational image compression with a scale hyperprior
13 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies in the latent representation. This hyperprior relates to side information, a concept universal to virtually all modern image codecs, but largely unexplored in image compression using artificial neural networks (ANNs). Unlike existing autoencoder compression methods, our model trains a complex prior jointly with the underlying autoencoder. We demonstrate that this model leads to state-of-the-art image compression when measuring visual quality using the popular MS-SSIM index, and yields rate-distortion performance surpassing published ANN-based methods when evaluated using a more traditional metric based on squared error (PSNR). Furthermore, we provide a qualitative comparison of models trained for different distortion metrics.
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representative citing papers
DeepCMC is a convolutional autoencoder architecture that compresses CSI matrices while jointly optimizing compression rate and reconstruction quality, outperforming prior schemes at equivalent bit rates.
GS-SCNet unifies 3D Gaussian Splatting with a disparity-guided semantic codec and direct Gaussian parameter prediction for efficient real-time 3D video communications with strong generalization.
Introduces a benchmark for VLMs on compressed images and a universal adaptor to improve performance across codecs and bitrates.
RDJSCC introduces CVIE and CCF mechanisms to leverage source correlation for better image reconstruction in distributed transmission over severe fading channels with imperfect CSI.
W-IR is the first watermarking framework to combine certified robustness via randomized smoothing in pixel and coordinate spaces with identity leakage mitigation via residual information loss minimization.
RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
Proposes T-GEMs plus a rate-based regularizer for early exits in CLIP encoders guided by text semantics to lower encoder usage costs.
ML-CLIPSim aggregates multi-layer patch and global similarities from frozen CLIP to approximate machine utility for images and outperforms standard IQA metrics on machine-preference tasks while staying competitive on human data.
A generative compression model using historical priors for Earth observation data achieves up to 10,000x reduction after exascale training on an Armv9 supercomputer.
SAMIC introduces semantic-aware Mamba blocks and SVD-based redundancy reduction to achieve efficient perceptual image compression with improved rate-distortion-perception tradeoffs.
PAT-VCM adds lightweight auxiliary tokens to a shared baseline video stream to support multiple downstream machine tasks without task-specific codecs.
Autoencoder CSI compression reduces channel sounding overhead by more than 50% versus standard IEEE 802.11 methods and improves throughput and latency in coordinated beamforming.
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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Deep Convolutional Compression for Massive MIMO CSI Feedback
DeepCMC is a convolutional autoencoder architecture that compresses CSI matrices while jointly optimizing compression rate and reconstruction quality, outperforming prior schemes at equivalent bit rates.
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Generalizable 3D Gaussian Splatting enabled Semantic Coding for Real-Time Immersive Video Communications
GS-SCNet unifies 3D Gaussian Splatting with a disparity-guided semantic codec and direct Gaussian parameter prediction for efficient real-time 3D video communications with strong generalization.
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Benchmarking and Enhancing VLM for Compressed Image Understanding
Introduces a benchmark for VLMs on compressed images and a universal adaptor to improve performance across codecs and bitrates.
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Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information
RDJSCC introduces CVIE and CCF mechanisms to leverage source correlation for better image reconstruction in distributed transmission over severe fading channels with imperfect CSI.
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"Training robust watermarking model may hurt authentication!'' Exploring and Mitigating the Identity Leakage in Robust Watermarking
W-IR is the first watermarking framework to combine certified robustness via randomized smoothing in pixel and coordinate spaces with identity leakage mitigation via residual information loss minimization.
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Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
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t-gems: text-guided exit modules for decreasing clip image encoder
Proposes T-GEMs plus a rate-based regularizer for early exits in CLIP encoders guided by text semantics to lower encoder usage costs.
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ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
ML-CLIPSim aggregates multi-layer patch and global similarities from frozen CLIP to approximate machine utility for images and outperforms standard IQA metrics on machine-preference tasks while staying competitive on human data.
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Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
A generative compression model using historical priors for Earth observation data achieves up to 10,000x reduction after exascale training on an Armv9 supercomputer.
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SAMIC: A Lightweight Semantic-Aware Mamba for Efficient Perceptual Image Compression
SAMIC introduces semantic-aware Mamba blocks and SVD-based redundancy reduction to achieve efficient perceptual image compression with improved rate-distortion-perception tradeoffs.
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PAT-VCM: Plug-and-Play Auxiliary Tokens for Video Coding for Machines
PAT-VCM adds lightweight auxiliary tokens to a shared baseline video stream to support multiple downstream machine tasks without task-specific codecs.
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Autoencoder-Based CSI Compression for Beyond Wi-Fi 8 Coordinated Beamforming
Autoencoder CSI compression reduces channel sounding overhead by more than 50% versus standard IEEE 802.11 methods and improves throughput and latency in coordinated beamforming.