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 651–700 of 1008 papers

TitleStatusHype
Exploring Structural Sparsity in Neural Image Compression—0
Recognition-Aware Learned Image Compression—0
Semantic-assisted image compression—0
An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems—0
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder—0
Deep Decoding of _-coded Light Field Images—0
Learning-Driven Lossy Image Compression; A Comprehensive Survey—0
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval—0
Learning True Rate-Distortion-Optimization for End-To-End Image Compression—0
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Deep Stereo Image Compression via Bi-Directional Coding—0
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification—0
A Compact Neural Network-based Algorithm for Robust Image Watermarking—0
A New Image Codec Paradigm for Human and Machine Uses—0
Adaptation and Attention for Neural Video Coding—0
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning—0
A New Approach to Image Compression in Industrial Internet of Things—0
Implicit Neural Representations for Image Compression—0
Strategies in JPEG compression using Convolutional Neural Network (CNN)—0
Using Image Transformations to Learn Network Structure—0
Low-complexity Rounded KLT Approximation for Image Compression—0
A Novel Framework for Image-to-image Translation and Image Compression—0
Universal Efficient Variable-rate Neural Image Compression—0
End-to-end optimized image compression with competition of prior distributionsCode0
End-to-End Learned Image Compression with Quantized Weights and Activations—0
Online Meta Adaptation for Variable-Rate Learned Image Compression—0
Transformer-based Image Compression—0
Learned Image Compression for Machine Perception—0
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder—0
Learned Image Compression with Separate Hyperprior Decoders—0
Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics—0
KDSalBox: A toolbox of efficient knowledge-distilled saliency models—0
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery—0
Image Compression and Classification Using Qubits and Quantum Deep Learning—0
Learning Sparse Masks for Diffusion-based Image Inpainting—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
End-to-End Image Compression with Probabilistic Decoding—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Lidar Range Image Compression with Deep Delta Encoding—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform—0
Image Processing via Multilayer Graph Spectra—0
Lossless Image Compression Using a Multi-Scale Progressive Statistical Model—0
Lossy Medical Image Compression using Residual Learning-based Dual Autoencoder Model—0
Learned Image Coding for Machines: A Content-Adaptive Approach—0
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Quality and Complexity Assessment of Learning-Based Image Compression Solutions—0
OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression—0
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual ModulesCode0
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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