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
9 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.
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.
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.
citing papers explorer
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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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Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
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.