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 801–850 of 1008 papers

TitleStatusHype
Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition—0
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation—0
Neural Image Compression with Quantization Rectifier—0
Neural Inter-Frame Compression for Video Coding—0
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder—0
Neural Multi-scale Image Compression—0
Neural Space-filling Curves—0
Noise Dimension of GAN: An Image Compression Perspective—0
Non-local Attention Optimized Deep Image Compression—0
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications—0
Novel variational model for inpainting in the wavelet domain—0
Object-Based Image Coding: A Learning-Driven Revisit—0
One-Step Diffusion-Based Image Compression with Semantic Distillation—0
One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing—0
Online Meta Adaptation for Variable-Rate Learned Image Compression—0
On reducing the correlation of bottleneck representations in Autoencoders—0
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
On Uniform Scalar Quantization for Learned Image Compression—0
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery—0
Optical Flow and Mode Selection for Learning-based Video Coding—0
Optimising Different Feature Types for Inpainting-based Image Representations—0
Optimising Spatial and Tonal Data for PDE-based Inpainting—0
Optimized Structured Sparse Sensing Matrices for Compressive Sensing—0
Optimizing CNN Architectures for Advanced Thoracic Disease Classification—0
Optimizing Data Processing in Space for Object Detection in Satellite Imagery—0
Optimizing JPEG Quantization for Classification Networks—0
OSLO-IC: On-the-Sphere Learned Omnidirectional Image Compression with Attention Modules and Spatial Context—0
OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression—0
Overfitted image coding at reduced complexity—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Overview of Variable Rate Coding in JPEG AI—0
Parallel Multiscale Autoregressive Density Estimation—0
Parallel Wavelet Schemes for Images—0
Pathology Image Compression with Pre-trained Autoencoders—0
Perceptual Image Compression with Cooperative Cross-Modal Side Information—0
Perceptual Learned Image Compression via End-to-End JND-Based Optimization—0
Perceptually Optimizing Deep Image Compression—0
Perceptual Quality Assessment for Fine-Grained Compressed Images—0
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
PICD: Versatile Perceptual Image Compression with Diffusion Rendering—0
Place-specific Background Modeling Using Recursive Autoencoders—0
Point Cloud-Assisted Neural Image Compression—0
Metaheuristic-based Energy-aware Image Compression for Mobile App Development—0
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint—0
Powerful Lossy Compression for Noisy Images—0
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain—0
Pre-demosaic Graph-based Light Field 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