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 201–250 of 1008 papers

TitleStatusHype
An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks—0
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal—0
3-D Context Entropy Model for Improved Practical Image Compression—0
An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU—0
DLF: Extreme Image Compression with Dual-generative Latent Fusion—0
DNN-Compressed Domain Visual Recognition with Feature Adaptation—0
Batch Normalization Alleviates the Spectral Bias in Coordinate Networks—0
A Cross Channel Context Model for Latents in Deep Image Compression—0
Distributed Learning and Inference with Compressed Images—0
Analysis of Neural Image Compression Networks for Machine-to-Machine Communication—0
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger—0
Generative Imaging and Image Processing via Generative Encoder—0
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds—0
Distribution prediction for image compression: An experimental re-compressor for JPEG images—0
Does lossy image compression affect racial bias within face recognition?—0
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification—0
An Adaptive Statistical Non-uniform Quantizer for Detail Wavelet Components in Lossy JPEG2000 Image Compression—0
Decouple Distortion from Perception: Region Adaptive Diffusion for Extreme-low Bitrate Perception Image Compression—0
Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression—0
Automatic Text Line Segmentation Directly in JPEG Compressed Document Images—0
A Compression Objective and a Cycle Loss for Neural Image Compression—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
DCT Approximations Based on Chen's Factorization—0
Deep Convolutional Neural Network for Image Deconvolution—0
Deep Decoding of _-coded Light Field Images—0
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression—0
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression—0
Bandwidth-efficient Inference for Neural Image Compression—0
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?—0
Autoencoders with Variable Sized Latent Vector for Image Compression—0
Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images—0
Deep Image Compression Using Scene Text Quality Assessment—0
Data-Efficient Sequence-Based Visual Place Recognition with Highly Compressed JPEG Images—0
Autoencoder based image compression: can the learning be quantization independent?—0
A Multiparametric Class of Low-complexity Transforms for Image and Video Coding—0
Deep Learning-based Image Compression with Trellis Coded Quantization—0
Deep Lossless Image Compression via Masked Sampling and Coarse-to-Fine Auto-Regression—0
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization—0
DANICE: Domain adaptation without forgetting in neural image compression—0
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework—0
Deep Perceptual Compression—0
Deep Perceptual Image Quality Assessment for Compression—0
Deep Predictive Video Compression with Bi-directional Prediction—0
Deep Generative Video Compression—0
Deep Probabilistic Video Compression—0
Deep Sampling Networks—0
Customized OCT images compression scheme with deep neural network—0
DeepSIC: Deep Semantic Image Compression—0
Deep Spatial and Tonal Data Optimisation for Homogeneous Diffusion Inpainting—0
A Modified No Search Algorithm for Fractal 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