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 951–975 of 1008 papers

TitleStatusHype
FFCA-Net: Stereo Image Compression via Fast Cascade Alignment of Side Information—0
Fine color guidance in diffusion models and its application to image compression at extremely low bitrates—0
F-INR: Functional Tensor Decomposition for Implicit Neural Representations—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Flexible Neural Image Compression via Code Editing—0
FLLIC: Functionally Lossless Image Compression—0
Flow-Lenia.png: Evolving Multi-Scale Complexity by Means of Compression—0
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization—0
Forward Error Correction applied to JPEG-XS codestreams—0
Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression—0
Fourier Spectrum Discrepancies in Deep Network Generated Images—0
Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression—0
FPGA-based Binocular Image Feature Extraction and Matching System—0
FPGA based Parallelized Architecture of Efficient Graph based Image Segmentation Algorithm—0
Fractional Calculus In Image Processing: A Review—0
Frequency-aware Learned Image Compression for Quality Scalability—0
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression—0
Frequency-Biased Synergistic Design for Image Compression and Compensation—0
Frequency Disentangled Features in Neural Image Compression—0
Full Quaternion Representation of Color images: A Case Study on QSVD-based Color Image Compression—0
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting—0
Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP—0
GAN-based Image Compression with Improved RDO Process—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