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 476–500 of 1008 papers

TitleStatusHype
Image compression optimized for 3D reconstruction by utilizing deep neural networks—0
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering—0
Image Compression: Sparse Coding vs. Bottleneck Autoencoders—0
Image Compression Using Novel View Synthesis Priors—0
Image Compression using only Attention based Neural Networks—0
A Lightweight Model for Perceptual Image Compression via Implicit Priors—0
Image Compression with Isotropic and Anisotropic Shepard Inpainting—0
A^2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems—0
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization—0
Generative Imaging and Image Processing via Generative Encoder—0
Image Compression with SVD : A New Quality Metric Based On Energy Ratio—0
Flexible Neural Image Compression via Code Editing—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
Content Adaptive Optimization for Neural Image Compression—0
Image Storage on Synthetic DNA Using Autoencoders—0
Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders—0
AsymLLIC: Asymmetric Lightweight Learned Image Compression—0
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Implicit Neural Multiple Description for DNA-based data storage—0
Implicit Neural Representations for Image Compression—0
Content Adaptive Latents and Decoder for Neural 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