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 476–500 of 1008 papers

TitleStatusHype
Image compression optimized for 3D reconstruction by utilizing deep neural networks—0
End-to-end optimized image compression for machines, a study—0
Image Compression: Sparse Coding vs. Bottleneck Autoencoders—0
Combined Approach for Image Segmentation—0
Image Compression using only Attention based Neural Networks—0
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework—0
Image Compression with Isotropic and Anisotropic Shepard Inpainting—0
Deep Perceptual Compression—0
Image Compression with Product Quantized Masked Image Modeling—0
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification—0
Image Compression with SVD : A New Quality Metric Based On Energy Ratio—0
Image-Dependent Local Entropy Models for Learned Image Compression—0
Image Distortion Detection using Convolutional Neural Network—0
Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks—0
Image-GS: Content-Adaptive Image Representation via 2D Gaussians—0
Image Restoration by Estimating Frequency Distribution of Local Patches—0
End-to-End Learning of Video Compression Using Spatio-Temporal Autoencoders—0
Image Storage on Synthetic DNA Using Autoencoders—0
Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders—0
Impact of Image Compression on In Vitro Cell Migration Analysis—0
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction—0
Color Learning for Image Compression—0
Implicit Neural Multiple Description for DNA-based data storage—0
Implicit Neural Representations for Image Compression—0
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces—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