SOTAVerified

Image Compression

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

Papers

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Showing 501–525 of 1008 papers

TitleStatusHype
Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders—0
MQ-Coder inspired arithmetic coder for synthetic DNA data storage—0
GAN-based Image Compression with Improved RDO Process—0
Exploring Resolution Fields for Scalable Image Compression with Uncertainty GuidanceCode0
Localization of Just Noticeable Difference for Image CompressionCode0
Exploring Effective Mask Sampling Modeling for Neural Image Compression—0
HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods—0
Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations—0
High-Fidelity Image Compression with Score-based Generative Models—0
Random-Access Neural Compression of Material Textures—0
Make Lossy Compression Meaningful for Low-Light ImagesCode0
Deep Image Compression Using Scene Text Quality Assessment—0
VVC+M: Plug and Play Scalable Image Coding for Humans and Machines—0
Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images—0
DNN-Compressed Domain Visual Recognition with Feature Adaptation—0
ROI-based Deep Image Compression with Swin Transformers—0
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression—0
Multiscale Augmented Normalizing Flows for Image Compression—0
Multi-Modality Deep Network for JPEG Artifacts Reduction—0
Semantically Structured Image Compression via Irregular Group-Based Decoupling—0
Impact of Image Compression on In Vitro Cell Migration Analysis—0
ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training—0
Learned Focused Plenoptic Image Compression with Microimage Preprocessing and Global AttentionCode0
Multi-Modality Deep Network for Extreme Learned Image Compression—0
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression—0
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All datasetskodakImageNet32BSDS500Caltech101Cars-196CIFAR-10FER2013Food-101Oxford-IIIT Pet DatasetPCamSTL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1STFBD-Rate over VTM-17.0-2.48—Unverified
2WACNNBD-Rate over VTM-17.0-2.95—Unverified
3ELICBD-Rate over VTM-17.0-5.95—Unverified
4MLICBD-Rate over VTM-17.0-8.05—Unverified
5SegPICBD-Rate over VTM-17.0-8.18—Unverified
6LIC-TCM LargeBD-Rate over VTM-17.0-10.14—Unverified
7MLIC+BD-Rate over VTM-17.0-11.39—Unverified
8MLIC++BD-Rate over VTM-17.0-13.39—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGbpsp6.42—Unverified
2JPEG2000bpsp6.35—Unverified
3L3Cbpsp4.76—Unverified
4MS-PixelCNNbpsp3.95—Unverified
5iFlowbpsp3.88—Unverified
#ModelMetricClaimedVerifiedStatus
1RK-CCSNetAverage PSNR30.51—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,340—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,470—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,410—Unverified
#ModelMetricClaimedVerifiedStatus
1SUDHEER10%1—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,270—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,210—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,490—Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,340—Unverified