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 626–650 of 1008 papers

TitleStatusHype
Image Compression and Classification Using Qubits and Quantum Deep Learning—0
Quantum pixel representations and compression for N-dimensional imagesCode1
Learning Sparse Masks for Diffusion-based Image Inpainting—0
End-to-End Image Compression with Probabilistic Decoding—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Transformer-based Transform CodingCode1
Lidar Range Image Compression with Deep Delta Encoding—0
Towards Flexible Blind JPEG Artifacts RemovalCode1
Unified Signal Compression Using a GAN with Iterative Latent Representation OptimizationCode1
Interpolation variable rate image compressionCode1
iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform—0
Image Compression with Recurrent Neural Network and Generalized Divisive NormalizationCode1
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
Supervised Compression for Resource-Constrained Edge Computing SystemsCode1
Variable-Rate Deep Image Compression through Spatially-Adaptive Feature TransformCode1
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Towards Top-Down Just Noticeable Difference Estimation of Natural ImagesCode1
Enhanced Invertible Encoding for Learned Image CompressionCode1
OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression—0
Quality and Complexity Assessment of Learning-Based Image Compression Solutions—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