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 451–500 of 1008 papers

TitleStatusHype
Understanding the Vulnerability of CLIP to Image CompressionCode0
Perceptual Image Compression with Cooperative Cross-Modal Side Information—0
Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression—0
Cattle Identification Using Muzzle Images and Deep Learning TechniquesCode0
Neural-based Compression Scheme for Solar Image Data—0
Deep learning based Image Compression for Microscopy Images: An Empirical StudyCode0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—0
NIF: A Fast Implicit Image Compression with Bottleneck Layers and Modulated Sinusoidal ActivationsCode0
Diagnosis-oriented Medical Image Compression with Efficient Transfer Learning—0
Towards Exploring Fairness in Visual Transformer based Natural and GAN Image Detection SystemsCode0
Image Compression using only Attention based Neural Networks—0
Distribution prediction for image compression: An experimental re-compressor for JPEG images—0
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression—0
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast MammographyCode0
Intelligent DRL-Based Adaptive Region of Interest for Delay-sensitive Telemedicine Applications—0
CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression—0
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution—0
On Uniform Scalar Quantization for Learned Image Compression—0
Linear Progressive Coding for Semantic Communication using Deep Neural Networks—0
Advancing The Rate-Distortion-Computation Frontier For Neural Image Compression—0
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting—0
Transformer-based Image Compression with Variable Image Quality Objectives—0
Neural Image Compression Using Masked Sparse Visual Representation—0
Multi-spectral Entropy Constrained Neural Compression of Solar Imagery—0
Multi-Context Dual Hyper-Prior Neural Image Compression—0
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