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


Use these libraries to find Image Compression models and implementations

Most implemented papers

Variational image compression with a scale hyperprior

InterDigitalInc/CompressAI ICLR 2018

We describe an end-to-end trainable model for image compression based on variational autoencoders.

End-to-end Optimized Image Compression

tensorflow/models 5 Nov 2016

We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.

Full Resolution Image Compression with Recurrent Neural Networks

tensorflow/models CVPR 2017

As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.

"Zero-Shot" Super-Resolution using Deep Internal Learning

assafshocher/ZSSR 17 Dec 2017

On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.

QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression

bytedance/qraf 10 Mar 2023

In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model.

An End-to-End Compression Framework Based on Convolutional Neural Networks

compression-framework/compression_framwork_for_tesing 2 Aug 2017

The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.

ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding

VincentChandelier/ELiC-ReImplemetation CVPR 2022

Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders.

Lossy Image Compression with Compressive Autoencoders

alexandru-dinu/cae 1 Mar 2017

We propose a new approach to the problem of optimizing autoencoders for lossy image compression.

High-Fidelity Generative Image Compression

tensorflow/compression NeurIPS 2020

We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system.

Semantic Perceptual Image Compression using Deep Convolution Networks

iamaaditya/image-compression-cnn 27 Dec 2016

Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression.