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 301–350 of 1008 papers

TitleStatusHype
FLLIC: Functionally Lossless Image Compression—0
Conditional Hallucinations for Image Compression—0
Conditional Coding for Flexible Learned Video Compression—0
A Specific Task-oriented Semantic Image Communication System for substation patrol inspection—0
Concatenated Attention Neural Network for Image Restoration—0
Computationally Efficient Neural Image Compression—0
A Spatial RNN Codec for End-to-End Image Compression—0
Computational ghost imaging with hybrid transforms by integrating Hadamard, discrete cosine, and Haar matrices—0
Compression without Quantization—0
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Are Visual Recognition Models Robust to Image Compression?—0
AICT: An Adaptive Image Compression Transformer—0
A Class of DCT Approximations Based on the Feig-Winograd Algorithm—0
Flow-Lenia.png: Evolving Multi-Scale Complexity by Means of Compression—0
Compressing Weight-updates for Image Artifacts Removal Neural Networks—0
A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model—0
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods—0
Compressible and Searchable: AI-native Multi-Modal Retrieval System with Learned Image Compression—0
A Recent Survey on the Applications of Genetic Programming in Image Processing—0
AccelIR: Task-Aware Image Compression for Accelerating Neural Restoration—0
A Rate-Distortion-Classification Approach for Lossy Image Compression—0
Fine color guidance in diffusion models and its application to image compression at extremely low bitrates—0
Compressed image quality assessment using stacking—0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—0
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics—0
F-INR: Functional Tensor Decomposition for Implicit Neural Representations—0
Flexible Neural Image Compression via Code Editing—0
Evaluating the Practicality of Learned Image Compression—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance—0
Entropy Based Cartoon Texture Separation—0
EVC: Towards Real-Time Neural Image Compression with Mask Decay—0
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack—0
A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression—0
Explicit Residual-Based Scalable Image Coding for Humans and Machines—0
Exploiting context dependence for image compression with upsampling—0
Compressed sensing of astronomical images:orthogonal wavelets domains—0
Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image Compression—0
Few-Shot Domain Adaptation for Learned Image Compression—0
Exploring Autoencoder-based Error-bounded Compression for Scientific Data—0
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution—0
Exploring Effective Mask Sampling Modeling for Neural Image Compression—0
COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability—0
A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing—0
Exploring Structural Sparsity in Neural Image Compression—0
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing—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