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 151–200 of 1008 papers

TitleStatusHype
Learning Accurate Entropy Model with Global Reference for Image CompressionCode1
Compressing Images by Encoding Their Latent Representations with Relative Entropy CodingCode1
Learning to Improve Image Compression without Changing the Standard DecoderCode1
Fidelity-Controllable Extreme Image Compression with Generative Adversarial NetworksCode1
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed SensingCode1
Channel-wise Autoregressive Entropy Models for Learned Image CompressionCode1
Channel-Level Variable Quantization Network for Deep Image CompressionCode1
Locally Masked Convolution for Autoregressive ModelsCode1
High-Fidelity Generative Image CompressionCode1
Neural Sparse Representation for Image RestorationCode1
Improving Inference for Neural Image CompressionCode1
Variational Quantum Singular Value DecompositionCode1
Comparison of Image Quality Models for Optimization of Image Processing SystemsCode1
Learning Better Lossless Compression Using Lossy CompressionCode1
Learning to Structure an Image with Few ColorsCode1
Learning for Video Compression with Hierarchical Quality and Recurrent EnhancementCode1
Asymmetric Gained Deep Image Compression With Continuous Rate AdaptationCode1
Hierarchical Quantized AutoencodersCode1
Variational Bayesian QuantizationCode1
Learning End-to-End Lossy Image Compression: A BenchmarkCode1
A Unified End-to-End Framework for Efficient Deep Image CompressionCode1
Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded RepresentationsCode1
Image Segmentation Using Deep Learning: A SurveyCode1
Deep Image Compression using Decoder Side InformationCode1
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention ModulesCode1
Scale-wise Convolution for Image RestorationCode1
Context-adaptive Entropy Model for End-to-end Optimized Image CompressionCode1
Joint Autoregressive and Hierarchical Priors for Learned Image CompressionCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Variational image compression with a scale hyperpriorCode1
Efficient Nonlinear Transforms for Lossy Image CompressionCode1
Lossy Image Compression with Compressive AutoencodersCode1
End-to-end Optimized Image CompressionCode1
Full Resolution Image Compression with Recurrent Neural NetworksCode1
Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic InferenceCode0
Explicit Residual-Based Scalable Image Coding for Humans and Machines—0
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical ImagesCode0
Fast Training-free Perceptual Image Compression—0
ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image CompressionCode0
Breaking the Multi-Enhancement Bottleneck: Domain-Consistent Quality Enhancement for Compressed Images—0
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization—0
Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression—0
Flexible Mixed Precision Quantization for Learned Image CompressionCode0
Structured Pruning and Quantization for Learned Image CompressionCode0
Your Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate—0
Semantics-Guided Generative Image CompressionCode0
Synonymous Variational Inference for Perceptual Image CompressionCode0
Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution—0
Low-Rank Adaptation of Pre-trained Vision Backbones for Energy-Efficient Image Coding for MachineCode0
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics—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