Cool-chic 5.0 achieves 11% BD-rate savings over VVC with a decoder 250× less complex than modern autoencoders and 10× fewer encoding iterations than previous overfitted codecs.
Learned image compression with hierarchical progressive context modeling
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RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
UI-LIC is an open-source software framework that unifies training, inference, and evaluation of learned image compression models with traditional encoders via shared configuration and a GUI for metrics and subjective analysis.
citing papers explorer
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Cool-chic 5.0: Faster Encoding and Inter-Feature Entropy Modeling for Overfitted Image Compression
Cool-chic 5.0 achieves 11% BD-rate savings over VVC with a decoder 250× less complex than modern autoencoders and 10× fewer encoding iterations than previous overfitted codecs.
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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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UI-LIC: A Unified Framework for Evaluating Learned Image Compression Models
UI-LIC is an open-source software framework that unifies training, inference, and evaluation of learned image compression models with traditional encoders via shared configuration and a GUI for metrics and subjective analysis.