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
Frequency-Aware Transformer for Learned Image Compression
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years.
Matrix Compression via Randomized Low Rank and Low Precision Factorization
pilancilab/matrix-compressor
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NeurIPS 2023
We propose an algorithm that exploits this structure to obtain a low rank decomposition of any matrix $\mathbf{A}$ as $\mathbf{A} \approx \mathbf{L}\mathbf{R}$, where $\mathbf{L}$ and $\mathbf{R}$ are the low rank factors.
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography
neogeoss/EMBED_Mammo_Models
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This research presents a novel framework for the compression and decompression of medical images utilizing the Latent Diffusion Model (LDM).
HAT: Hybrid Attention Transformer for Image Restoration
chxy95/hat
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In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement.
EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation
We introduce EGIC, an enhanced generative image compression method that allows traversing the distortion-perception curve efficiently from a single model.
An Improved Upper Bound on the Rate-Distortion Function of Images
Recent work has shown that Variational Autoencoders (VAEs) can be used to upper-bound the information rate-distortion (R-D) function of images, i. e., the fundamental limit of lossy image compression.
CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images
The proposal demonstrates that the data compression achieved via fractal segmentation preprocessing yields enhanced image compression results while remaining lossless in its reconstruction accuracy.
RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionar
Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet) is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs.
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
llvy21/duic
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We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation.
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Additionally, to capture global contexts, we propose the linear complexity attention-based global correlations capturing by leveraging the decomposition of the softmax operation.
