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 501–550 of 1008 papers

TitleStatusHype
Optimising Different Feature Types for Inpainting-based Image Representations—0
Optimising Spatial and Tonal Data for PDE-based Inpainting—0
Optimized Structured Sparse Sensing Matrices for Compressive Sensing—0
Optimizing CNN Architectures for Advanced Thoracic Disease Classification—0
Optimizing Data Processing in Space for Object Detection in Satellite Imagery—0
Optimizing JPEG Quantization for Classification Networks—0
OSLO-IC: On-the-Sphere Learned Omnidirectional Image Compression with Attention Modules and Spatial Context—0
OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression—0
Overfitted image coding at reduced complexity—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Overview of Variable Rate Coding in JPEG AI—0
Parallel Multiscale Autoregressive Density Estimation—0
Parallel Wavelet Schemes for Images—0
Pathology Image Compression with Pre-trained Autoencoders—0
Perceptual Image Compression with Cooperative Cross-Modal Side Information—0
Perceptual Learned Image Compression via End-to-End JND-Based Optimization—0
Perceptually Optimizing Deep Image Compression—0
Perceptual Quality Assessment for Fine-Grained Compressed Images—0
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
PICD: Versatile Perceptual Image Compression with Diffusion Rendering—0
Place-specific Background Modeling Using Recursive Autoencoders—0
Point Cloud-Assisted Neural Image Compression—0
Metaheuristic-based Energy-aware Image Compression for Mobile App Development—0
Post-Training Quantization for Cross-Platform Learned Image Compression—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint—0
Powerful Lossy Compression for Noisy Images—0
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain—0
Pre-demosaic Graph-based Light Field Image Compression—0
Preprocessing Enhanced Image Compression for Machine Vision—0
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction—0
Probing Image Compression For Class-Incremental Learning—0
Processing Energy Modeling for Neural Network Based Image Compression—0
ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training—0
Progressive Compression with Universally Quantized Diffusion Models—0
Progressive Feature Fusion Network for Enhancing Image Quality Assessment—0
Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression—0
Progressive Neural Image Compression with Nested Quantization and Latent Ordering—0
Q-LIC: Quantizing Learned Image Compression with Channel Splitting—0
Quality and Complexity Assessment of Learning-Based Image Compression Solutions—0
Quantifying the effect of image compression on supervised learning applications in optical microscopy—0
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction—0
Quantum Implicit Neural Compression—0
RAGE for the Machine: Image Compression with Low-Cost Random Access for Embedded Applications—0
Random-Access Neural Compression of Material Textures—0
Rate Distortion Characteristic Modeling for Neural Image Compression—0
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
RAWtoBit: A Fully End-to-end Camera ISP Network—0
Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization—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