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 701–750 of 1008 papers

TitleStatusHype
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
CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI FeedbackCode1
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
Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding—0
Soft Compression for Lossless Image Coding—0
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs—0
Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling CurvesCode0
Causal Contextual Prediction for Learned Image Compression—0
End-to-end optimized image compression for machines, a study—0
CompressAI: a PyTorch library and evaluation platform for end-to-end compression researchCode1
A Systematic Evaluation of Coding Strategies for Sparse Binary Images—0
Learning Accurate Entropy Model with Global Reference for Image CompressionCode1
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding—0
Descriptive analysis of computational methods for automating mammograms with practical applications—0
Compressing Images by Encoding Their Latent Representations with Relative Entropy CodingCode1
Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks—0
Learning to Improve Image Compression without Changing the Standard DecoderCode1
Quantifying the effect of image compression on supervised learning applications in optical microscopy—0
Extreme compression of grayscale images—0
Leveraging Domain Knowledge using Machine Learning for Image Compression in Internet-of-Things—0
Fidelity-Controllable Extreme Image Compression with Generative Adversarial NetworksCode1
Lossy Image Compression with Normalizing Flows—0
Optical Flow and Mode Selection for Learning-based Video Coding—0
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed SensingCode1
Task-Aware Quantization Network for JPEG Image Compression—0
Learning to Learn to Compress—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
Regularized Flexible Activation Function Combinations for Deep Neural Networks—0
Accelerating Deep Learning Applications in Space—0
Wireless Image Retrieval at the Edge—0
Full Quaternion Representation of Color images: A Case Study on QSVD-based Color Image Compression—0
Channel-wise Autoregressive Entropy Models for Learned Image CompressionCode1
Channel-Level Variable Quantization Network for Deep Image CompressionCode1
C2G-Net: Exploiting Morphological Properties for Image Classification—0
ModeNet: Mode Selection Network For Learned Video Coding—0
Perceptually Optimizing Deep Image Compression—0
Locally Masked Convolution for Autoregressive ModelsCode1
Modeling Lost Information in Lossy Image Compression—0
A Multiparametric Class of Low-complexity Transforms for Image and Video Coding—0
Concatenated Attention Neural Network for Image Restoration—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