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
Domain Adaptation for Learned Image Compression with Supervised Adapters
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet its performance may be far from optimal on images sampled from different domains.
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates.
Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks
This method transmits only the semantic information of an image, and the receiver reconstructs the image using an image-generation model.
Compressible and Searchable: AI-native Multi-Modal Retrieval System with Learned Image Compression
The burgeoning volume of digital content across diverse modalities necessitates efficient storage and retrieval methods.
MarsQE: Semantic-Informed Quality Enhancement for Compressed Martian Image
Lossy image compression is essential for Mars exploration missions, due to the limited bandwidth between Earth and Mars.
Lossy Image Compression with Foundation Diffusion Models
Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates.
Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control.
Learning to Classify New Foods Incrementally Via Compressed Exemplars
Therefore, food image classification systems should adapt to and manage data that continuously evolves.
Fine color guidance in diffusion models and its application to image compression at extremely low bitrates
This study addresses the challenge of, without training or fine-tuning, controlling the global color aspect of images generated with a diffusion model.
DiffHarmony: Latent Diffusion Model Meets Image Harmonization
To deal with these issues, in this paper, we first adapt a pre-trained latent diffusion model to the image harmonization task to generate the harmonious but potentially blurry initial images.
