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

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Showing 176–200 of 1008 papers

TitleStatusHype
Accelerating block-level rate control for learned image compression—0
Approximately Invertible Neural Network for Learned Image Compression—0
Quantization-aware Matrix Factorization for Low Bit Rate Image CompressionCode1
Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement—0
Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs—0
HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression—0
Flow-Lenia.png: Evolving Multi-Scale Complexity by Means of Compression—0
SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression—0
Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression—0
Bridging Compressed Image Latents and Multimodal Large Language Models—0
Scaling Training Data with Lossy Image CompressionCode0
Towards the Spectral bias Alleviation by Normalizations in Coordinate NetworksCode0
High Efficiency Image Compression for Large Visual-Language Models—0
FCNR: Fast Compressive Neural Representation of Visualization ImagesCode0
CIC: Circular Image Compression—0
Learned HDR Image Compression for Perceptually Optimal Storage and Display—0
Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image CompressionCode0
High Frequency Matters: Uncertainty Guided Image Compression with Wavelet DiffusionCode1
Neural Compression of Atmospheric States—0
Rethinking Learned Image Compression: Context is All You Need—0
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression—0
Bidirectional Stereo Image Compression with Cross-Dimensional Entropy ModelCode1
PSC: Posterior Sampling-Based CompressionCode1
Image Compression for Machine and Human Vision with Spatial-Frequency AdaptationCode2
OMR-NET: a two-stage octave multi-scale residual network for screen content image compressionCode0
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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