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.
End-to-end optimized image compression
11 Pith papers cite this work. Polarity classification is still indexing.
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SAD is a new explicit differentiable image representation based on soft anisotropic additively weighted Voronoi partitions that achieves higher PSNR and 4-19x faster training than Image-GS and Instant-NGP at matched bitrate.
NDGI compresses temporal lightmaps via neural feature maps and lightweight networks, delivering high-quality dynamic global illumination with low storage and modest real-time decompression cost.
Finite scalar quantization simplifies VQ-VAE latents by independently rounding a few dimensions to fixed levels, producing an equivalent-sized implicit codebook with competitive performance and no collapse.
RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
Derives optimality constraints for nonnegative joint dictionary learning that explain observed SAE behaviors such as feature splitting, absorption, and dense antipodal features.
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.
SAMIC introduces semantic-aware Mamba blocks and SVD-based redundancy reduction to achieve efficient perceptual image compression with improved rate-distortion-perception tradeoffs.
ActDiff-VC partitions video into segments, transmits adaptive keyframes and budget-aware point trajectories, and reconstructs frames via conditional diffusion, reporting up to 64.6% bitrate reduction at matched NIQE on UVG and MCL-JCV.
A bilinear CNN that fuses features from a distortion-type classifier and an image classifier achieves superior BIQA performance on both synthetic and authentic distortion databases.
DinoLink uses saliency-aware token pruning plus residual vector quantization to cut V2X bitrate by 139x while reporting 32.8% mAP on nuScenes.
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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.