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 351–375 of 1008 papers

TitleStatusHype
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression—0
Extending Unsupervised Neural Image Compression With Supervised Multitask Learning—0
Extreme compression of grayscale images—0
Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models—0
Extreme Image Compression using Fine-tuned VQGANs—0
Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization—0
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution—0
COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability—0
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing—0
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation—0
Enhancing the Rate-Distortion-Perception Flexibility of Learned Image Codecs with Conditional Diffusion Decoders—0
Fast Cosine Transform to increase speed-up and efficiency of Karhunen-Loeve Transform for lossy image compression—0
Fast Data-independent KLT Approximations Based on Integer Functions—0
An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding—0
Fast Training-free Perceptual Image Compression—0
Conditional Hallucinations for Image Compression—0
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression—0
Feature Compression for Rate Constrained Object Detection on the Edge—0
Conditional Perceptual Quality Preserving Image Compression—0
FEDS: Feature and Entropy-Based Distillation Strategy for Efficient Learned Image Compression—0
Few-Shot Domain Adaptation for Learned Image Compression—0
FFCA-Net: Stereo Image Compression via Fast Cascade Alignment of Side Information—0
Comparison of Lossless Image Formats—0
Fine color guidance in diffusion models and its application to image compression at extremely low bitrates—0
Approximately Invertible Neural Network for Learned 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