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 701–750 of 1008 papers

TitleStatusHype
Traditional Transformation Theory Guided Model for Learned Image Compression—0
Transferable Learned Image Compression-Resistant Adversarial Perturbations—0
Transformations in Learned Image Compression from a Modulation Perspective—0
Transformer-based Image Compression—0
Transformer-based Image Compression with Variable Image Quality Objectives—0
Transformer-based Learned Image Compression for Joint Decoding and Denoising—0
Transformer-Based Nonlinear Transform Coding for Multi-Rate CSI Compression in MIMO-OFDM Systems—0
Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement—0
TVC: Tokenized Video Compression with Ultra-Low Bitrate—0
Two-Layer Lossless HDR Coding using Histogram Packing Technique with Backward Compatibility to JPEG—0
Two-layer Near-lossless HDR Coding with Backward Compatibility to JPEG—0
UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding—0
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks—0
Ultra High Fidelity Image Compression with _-constrained Encoding and Deep Decoding—0
Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion—0
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation—0
Unified learning-based lossy and lossless JPEG recompression—0
Union is strength in lossy image compression—0
Universal Efficient Variable-rate Neural Image Compression—0
Universal End-to-End Neural Network for Lossy Image Compression—0
Universal Learned Image Compression With Low Computational Cost—0
Universal Representations for Classification-enhanced Lossy Compression—0
Upsampling Improvement for Overfitted Neural Coding—0
Using Image Transformations to Learn Network Structure—0
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding—0
Variable Rate Deep Image Compression With a Conditional Autoencoder—0
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets—0
Variable Rate Video Compression using a Hybrid Recurrent Convolutional Learning Framework—0
Variable-size Symmetry-based Graph Fourier Transforms for image compression—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Versatile Volumetric Medical Image Coding for Human-Machine Vision—0
Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Vision Transformer-based Semantic Communications With Importance-Aware Quantization—0
Visually Similar Products Retrieval for Shopsy—0
V-variable image compression—0
VVC+M: Plug and Play Scalable Image Coding for Humans and Machines—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection—0
Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields—0
Window-based Channel Attention for Wavelet-enhanced Learned Image Compression—0
Wireless Image Retrieval at the Edge—0
XAI based Performance Preserving Adaptive Image Compression for Efficient Satellite Communication—0
X-GANs: Image Reconstruction Made Easy for Extreme Cases—0
X-Ray Image Compression Using Convolutional Recurrent Neural Networks—0
You Only Train Once: Loss-Conditional Training of Deep Networks—0
Your Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate—0
LoC-LIC: Low Complexity Learned Image Coding Using Hierarchical Feature Transforms—0
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background—0
Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks—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