Image Compression
226 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
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Our framework can be conveniently used for both learned image compression (LIC) and learned video compression (LVC).
Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder
We build a diffusion model and design a novel paradigm that combines the diffusion model and an end-to-end decoder, and the latter is responsible for transmitting the privileged information extracted at the encoder side.
Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint
In recent years, large-scale adoption of cloud storage solutions has revolutionized the way we think about digital data storage.
Convolutional variational autoencoders for secure lossy image compression in remote sensing
The large amounts of data along with security concerns call for new compression and encryption techniques capable of preserving reconstruction quality while minimizing the transmission cost of this data back to Earth.
The Rate-Distortion-Perception Trade-off: The Role of Private Randomness
The per-symbol near-perfect realism constraint requires that the TVD between the distribution of output symbol $Y_t$ and the source distribution be arbitrarily small, uniformly in the index $t.$ We characterize the corresponding asymptotic rate-distortion trade-off and show that encoder private randomness is not useful if the compression rate is lower than the entropy of the source, however limited the resources in terms of common randomness and decoder private randomness may be.
Neural Image Compression with Quantization Rectifier
This paper presents a novel quantization rectifier (QR) method for image compression that leverages image feature correlation to mitigate the impact of quantization.
Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis
The global spatial context is built upon the Transformer, which is specifically designed for image compression tasks.
Powerful Lossy Compression for Noisy Images
Image compression and denoising represent fundamental challenges in image processing with many real-world applications.
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.
