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 901–925 of 1008 papers

TitleStatusHype
Efficient Masked Image Compression with Position-Indexed Self-Attention—0
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting—0
Efficient Parallel Data Optimization for Homogeneous Diffusion Inpainting of 4K Images—0
Emergence of Painting Ability via Recognition-Driven Evolution—0
End-to-End Image Compression with Probabilistic Decoding—0
End-to-End Learned Block-Based Image Compression with Block-Level Masked Convolutions and Asymptotic Closed Loop Training—0
End-to-End Learned Image Compression with Quantized Weights and Activations—0
End-to-End Learning of Video Compression Using Spatio-Temporal Autoencoders—0
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification—0
End-to-end optimized image compression for machines, a study—0
End-to-end optimized image compression for multiple machine tasks—0
End-to-End Optimized Image Compression with the Frequency-Oriented Transform—0
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning—0
Enhanced Residual SwinV2 Transformer for Learned Image Compression—0
Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks—0
Enhancement of theColor Image Compression Using a New Algorithm based on Discrete Hermite Wavelet Transform—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Enhancing Perception Quality in Remote Sensing Image Compression via Invertible Neural Network—0
Enhancing the Rate-Distortion-Perception Flexibility of Learned Image Codecs with Conditional Diffusion Decoders—0
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing—0
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution—0
Entropy Based Cartoon Texture Separation—0
Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance—0
Evaluating the Practicality of Learned Image Compression—0
EVC: Towards Real-Time Neural Image Compression with Mask Decay—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