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 701–750 of 1008 papers

TitleStatusHype
Optimizing Data Processing in Space for Object Detection in Satellite Imagery—0
Pragmatic Image Compression for Human-in-the-Loop Decision-MakingCode0
Intrinsic Image Transfer for Illumination ManipulationCode0
Cascade Decoders-Based Autoencoders for Image Reconstruction—0
Comparison of Lossless Image Formats—0
Rate Distortion Characteristic Modeling for Neural Image Compression—0
Learning-Based Practical Light Field Image Compression Using A Disparity-Aware ModelCode0
Wide Receptive Field and Channel Attention Network for JPEG Compressed Image DeblurringCode0
Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression—0
Reversible Colour Density Compression of Images using cGANsCode0
Universal Rate-Distortion-Perception Representations for Lossy CompressionCode0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
DeepCompress: Efficient Point Cloud Geometry CompressionCode0
CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network—0
Exploring Autoencoder-based Error-bounded Compression for Scientific Data—0
Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression—0
Coding Standards as Anchors for the CVPR CLIC video track—0
Substitutional Neural Image Compression—0
Neural 3D Scene Compression via Model Compression—0
Efficient Data Optimisation for Harmonic Inpainting with Finite Elements—0
A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression—0
DANICE: Domain adaptation without forgetting in neural image compression—0
Learning-based Compression for Material and Texture Recognition—0
Conditional Coding for Flexible Learned Video Compression—0
Soft then Hard: Rethinking the Quantization in Neural Image Compression—0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories—0
A Decade of Research for Image Compression In Multimedia Laboratory—0
BlinQS: Blind Quality Scalable Image Compression Algorithm without using PCRD Optimization—0
Learning Scalable _-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression—0
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton—0
Improved Autoregressive Modeling with Distribution Smoothing—0
Thousand to One: Semantic Prior Modeling for Conceptual Coding—0
End-to-end optimized image compression for multiple machine tasks—0
Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching—0
Hierarchical Image Compression Framework—0
On reducing the correlation of bottleneck representations in Autoencoders—0
A Cross Channel Context Model for Latents in Deep Image Compression—0
Deep Perceptual Image Quality Assessment for Compression—0
An Implementation of Vector Quantization using the Genetic Algorithm Approach—0
Pre-demosaic Graph-based Light Field Image Compression—0
Discrete Cosine Transform in JPEG Compression—0
Progressive Neural Image Compression with Nested Quantization and Latent Ordering—0
JPEG Meets PDE-based Image Compression—0
Image Compression with Encoder-Decoder Matched Semantic SegmentationCode0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition—0
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks—0
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality—0
Learned Block-based Hybrid 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