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
Most implemented papers
Efficient Nonlinear Transforms for Lossy Image Compression
We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
InterDigitalInc/CompressAI
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NeurIPS 2018
While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models.
Practical Full Resolution Learned Lossless Image Compression
fab-jul/L3C-PyTorch
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CVPR 2019
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.
Residual Dense Network for Image Restoration
yulunzhang/RDN
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We fully exploit the hierarchical features from all the convolutional layers.
Learning to compress and search visual data in large-scale systems
The problem of high-dimensional and large-scale representation of visual data is addressed from an unsupervised learning perspective.
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
ZhengxueCheng/Learned-Image-Compression-with-GMM-and-Attention
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In this paper, we explore the remaining redundancy of recent learned compression algorithms.
Deep Image Compression using Decoder Side Information
We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase.
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
RenYang-home/HLVC
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CVPR 2020
In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
InterDigitalInc/CompressAI
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This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs.
Checkerboard Context Model for Efficient Learned Image Compression
JiangWeibeta/Checkerboard-Context-Model-for-Efficient-Learned-Image-Compression
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CVPR 2021
To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression.
