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
Most implemented papers
Neural Distributed Image Compression using Common Information
ipc-lab/NDIC
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The received latent representation and the locally generated common information are passed through a decoder network to obtain an enhanced reconstruction of the input image.
Transformer-based Transform Coding
Nikolai10/SwinT-ChARM
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ICLR 2022
Neural data compression based on nonlinear transform coding has made great progress over the last few years, mainly due to improvements in prior models, quantization methods and nonlinear transforms.
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
zihaoliu123/Feature-Distillation-DNN-Oriented-JPEG-Compression-Against-Adversarial-Examples
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CVPR 2019
Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently.
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
makbari7/DSSLIC
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A compact representation of the input image is also generated and encoded as the first enhancement layer.
Context-adaptive Entropy Model for End-to-end Optimized Image Compression
JooyoungLeeETRI/CA_Entropy_Model
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ICLR 2019
We propose a context-adaptive entropy model for use in end-to-end optimized image compression.
Practical Lossless Compression with Latent Variables using Bits Back Coding
bits-back/bits-back
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ICLR 2019
Deep latent variable models have seen recent success in many data domains.
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
We introduce ADMM-pruned Compressive AutoEncoder (CAE-ADMM) that uses Alternative Direction Method of Multipliers (ADMM) to optimize the trade-off between distortion and efficiency of lossy image compression.
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
Image Segmentation Using Deep Learning: A Survey
Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others.
Learning End-to-End Lossy Image Compression: A Benchmark
huzi96/Coarse2Fine-PyTorch
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In this paper, we first conduct a comprehensive literature survey of learned image compression methods.
