Image Compression
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Benchmarks
kodak
ImageNet32
BSDS500
CIFAR-10
Caltech101
Cars-196
FER2013
Food-101
Oxford-IIIT Pet Dataset
PCam
STL-10
Most implemented
Variational image compression with a scale hyperprior
End-to-end Optimized Image Compression
"Zero-Shot" Super-Resolution using Deep Internal Learning
Full Resolution Image Compression with Recurrent Neural Networks
Papers
CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression
To reduce deployment cost and retraining overhead, adapting pretrained learned image compression (LIC) models to downstream machine vision tasks has attracted growing attention. However, existing methods typically insert…
parameter-efficient fine-tuningImage CompressionToken-Oriented Semantic Communication with Pretrained Vision Transformers
Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client--server collaborative inference in resource-constrained edge systems.…
Semantic CommunicationImage CompressionPractical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning
Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to…
Image CompressionGVCCTurbo: Rate-Compute Quality Scheduling for Codebook Driven Generative Compression
Codebook-driven generative compression uses a pretrained image or video generator as a zero-shot visual prior and transmits compact codebook indices to guide reconstruction at ultra-low bitrate. Current codecs tie each f…
Image CompressionMixCompress: Mixture of Experts for Variable Rate Learned Image Compression
Learned image compression (LIC) is bottlenecked by the need to store independent models for each rate-distortion operating point. Existing variable bit-rate (VBR) methods aim to reduce this overhead via dense parameter m…
Image CompressionLUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression
Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained model, converting pixel values into token sequences that the LLM proces…
Image Compression