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 301–350 of 1008 papers

TitleStatusHype
Efficient Progressive Image Compression with Variance-aware MaskingCode0
JPEG AI Image Compression Visual Artifacts: Detection Methods and Dataset—0
Enhancing Learned Image Compression via Cross Window-based AttentionCode0
Conditional Hallucinations for Image Compression—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Learning Lossless Compression for High Bit-Depth Volumetric Medical Image—0
Test-time adaptation for image compression with distribution regularization—0
Towards Reproducible Learning-based Compression—0
Fine-grained subjective visual quality assessment for high-fidelity compressed imagesCode0
Fast Data-independent KLT Approximations Based on Integer Functions—0
Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression—0
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model—0
JPEG Inspired Deep LearningCode0
Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach—0
GABIC: Graph-based Attention Block for Image CompressionCode0
STanH : Parametric Quantization for Variable Rate Learned Image Compression—0
Learning-Based Image Compression for MachinesCode0
Streaming Neural Images—0
The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning—0
Window-based Channel Attention for Wavelet-enhanced Learned Image Compression—0
Reduced bit median quantization: A middle process for Efficient Image Compression—0
Multi-Scale Feature Prediction with Auxiliary-Info for Neural Image Compression—0
Breaking the Barriers of One-to-One Usage of Implicit Neural Representation in Image Compression: A Linear Combination Approach with Performance GuaranteesCode0
LFIC-DRASC: Deep Light Field Image Compression Using Disentangled Representation and Asymmetrical Strip Convolution—0
Few-Shot Domain Adaptation for Learned Image Compression—0
Edge-based Denoising Image Compression—0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Universal End-to-End Neural Network for Lossy Image Compression—0
A Taxonomy of Miscompressions: Preparing Image Forensics for Neural Compression—0
Convolutional Transformer-Based Image Compression—0
Efficient Image Compression Using Advanced State Space Models—0
Compressed learning based onboard semantic compression for remote sensing platformsCode0
Accelerating block-level rate control for learned image compression—0
Approximately Invertible Neural Network for Learned Image Compression—0
Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement—0
HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression—0
Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs—0
SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression—0
Flow-Lenia.png: Evolving Multi-Scale Complexity by Means of Compression—0
Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression—0
Bridging Compressed Image Latents and Multimodal Large Language Models—0
Towards the Spectral bias Alleviation by Normalizations in Coordinate NetworksCode0
Scaling Training Data with Lossy Image CompressionCode0
High Efficiency Image Compression for Large Visual-Language Models—0
FCNR: Fast Compressive Neural Representation of Visualization ImagesCode0
Learned HDR Image Compression for Perceptually Optimal Storage and Display—0
CIC: Circular Image Compression—0
Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image CompressionCode0
Neural Compression of Atmospheric States—0
Rate-Distortion-Cognition Controllable Versatile Neural 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