Image Compression
227 papers with code • 11 benchmarks • 11 datasets
Image Compression is an application of data compression for digital images to lower their storage and/or transmission requirements.
Source: Variable Rate Deep Image Compression With a Conditional Autoencoder
Benchmarks
These leaderboards are used to track progress in Image Compression
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Libraries
Use these libraries to find Image Compression models and implementationsDatasets
CIFAR-10
STL-10
Food-101
BSDS500
FER2013
ImageNet-32
PCam
Oxford-IIIT Pet Dataset
Oxford-IIIT Pets
Subtasks
Latest papers with no code
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization
MSE-based models aim to improve objective metrics while generative models are leveraged to improve visual quality measured by subjective metrics.
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance.
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
In the proposed scheme, the source transmits information in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal to be conveyed to the destination.
Noise Dimension of GAN: An Image Compression Perspective
This trade-off depicts the best divergence we can achieve when noise is limited.
Content-aware Masked Image Modeling Transformer for Stereo Image Compression
Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations.
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression
Adversarial training has been validated in image compression models as a common method to enhance model robustness.
Probing Image Compression For Class-Incremental Learning
To this end, we introduce a new framework to incorporate image compression for continual ML including a pre-processing data compression step and an efficient compression rate/algorithm selection method.
Image Coding for Machines with Edge Information Learning Using Segment Anything
We also show that SA-NeRV is superior to ordinary NeRV in video compression for machines.
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images.
Enhancing the Rate-Distortion-Perception Flexibility of Learned Image Codecs with Conditional Diffusion Decoders
Learned image compression codecs have recently achieved impressive compression performances surpassing the most efficient image coding architectures.
