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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 1008 papers

TitleStatusHype
Implicit Neural Multiple Description for DNA-based data storage—0
COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability—0
Bandwidth-efficient Inference for Neural Image Compression—0
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation—0
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack—0
Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance—0
Image Coding for Machines with Object Region Learning—0
Efficient Learned Lossless JPEG Recompression—0
CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical ImagesCode0
Enhanced Residual SwinV2 Transformer for Learned Image Compression—0
Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression—0
RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature DictionarCode0
Conditional Perceptual Quality Preserving Image Compression—0
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification—0
Frequency Disentangled Features in Neural Image Compression—0
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones—0
Extreme Image Compression using Fine-tuned VQGANs—0
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression—0
AICT: An Adaptive Image Compression Transformer—0
Spatially-Adaptive Learning-Based Image Compression with Hierarchical Multi-Scale Latent Spaces—0
Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression—0
Color Learning for Image Compression—0
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural RepresentationCode0
Processing Energy Modeling for Neural Network Based Image Compression—0
Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in 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