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

Showing 151175 of 1008 papers

TitleStatusHype
CMC-Bench: Towards a New Paradigm of Visual Signal CompressionCode1
Dynamic Low-Rank Instance Adaptation for Universal Neural Image CompressionCode1
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image CompressionCode1
COIN: COmpression with Implicit Neural representationsCode1
ECSIC: Epipolar Cross Attention for Stereo Image CompressionCode1
Efficient Nonlinear Transforms for Lossy Image CompressionCode1
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image CompressionCode1
Multi-Realism Image Compression with a Conditional GeneratorCode1
Boosting Neural Image Compression for Machines Using Latent Space MaskingCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Expanded Adaptive Scaling Normalization for End to End Image CompressionCode1
Fitted Neural Lossless Image CompressionCode1
Neural Sparse Representation for Image RestorationCode1
Entroformer: A Transformer-based Entropy Model for Learned Image CompressionCode1
COSMIC: Compress Satellite Images Efficiently via Diffusion CompensationCode1
CompressAI: a PyTorch library and evaluation platform for end-to-end compression researchCode1
End-to-end Optimized Image CompressionCode1
Optimizing Image Compression via Joint Learning with DenoisingCode1
Learning for Video Compression with Hierarchical Quality and Recurrent EnhancementCode1
M2TR: Multi-modal Multi-scale Transformers for Deepfake DetectionCode1
Neural Image Compression: Generalization, Robustness, and Spectral BiasesCode1
FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering DeepfakesCode1
Compressing Images by Encoding Their Latent Representations with Relative Entropy CodingCode1
Fidelity-Controllable Extreme Image Compression with Generative Adversarial NetworksCode1
SASIC: Stereo Image Compression With Latent Shifts and Stereo AttentionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1STFBD-Rate over VTM-17.0-2.48Unverified
2WACNNBD-Rate over VTM-17.0-2.95Unverified
3ELICBD-Rate over VTM-17.0-5.95Unverified
4MLICBD-Rate over VTM-17.0-8.05Unverified
5SegPICBD-Rate over VTM-17.0-8.18Unverified
6LIC-TCM LargeBD-Rate over VTM-17.0-10.14Unverified
7MLIC+BD-Rate over VTM-17.0-11.39Unverified
8MLIC++BD-Rate over VTM-17.0-13.39Unverified
#ModelMetricClaimedVerifiedStatus
1PNGbpsp6.42Unverified
2JPEG2000bpsp6.35Unverified
3L3Cbpsp4.76Unverified
4MS-PixelCNNbpsp3.95Unverified
5iFlowbpsp3.88Unverified
#ModelMetricClaimedVerifiedStatus
1RK-CCSNetAverage PSNR30.51Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,340Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,470Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,410Unverified
#ModelMetricClaimedVerifiedStatus
1SUDHEER10%1Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,270Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,210Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,490Unverified
#ModelMetricClaimedVerifiedStatus
1Lossyless CompressorBit rate1,340Unverified