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 751–800 of 1008 papers

TitleStatusHype
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—0
A Rate-Distortion-Classification Approach for Lossy Image Compression—0
A Recent Survey on the Applications of Genetic Programming in Image Processing—0
A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing—0
Are Visual Recognition Models Robust to Image Compression?—0
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms—0
A Spatial RNN Codec for End-to-End Image Compression—0
A Specific Task-oriented Semantic Image Communication System for substation patrol inspection—0
A study for Image compression using Re-Pair algorithm—0
A Study on the Effect of Color Spaces in Learned Image Compression—0
A study on the use of perceptual hashing to detect manipulation of embedded messages in images—0
AsymLLIC: Asymmetric Lightweight Learned Image Compression—0
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering—0
A Systematic Evaluation of Coding Strategies for Sparse Binary Images—0
A Taxonomy of Miscompressions: Preparing Image Forensics for Neural Compression—0
A Training-Free Defense Framework for Robust Learned Image Compression—0
Attack and Defense Analysis of Learned Image Compression—0
Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory—0
Attention Based Image Compression Post-Processing Convolutional Neural Network—0
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton—0
A Unified Image Preprocessing Framework For Image Compression—0
Autoencoder based image compression: can the learning be quantization independent?—0
Autoencoders with Variable Sized Latent Vector for Image Compression—0
Automatic Text Line Segmentation Directly in JPEG Compressed Document Images—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger—0
Bandwidth-efficient Inference for Neural Image Compression—0
Batch Normalization Alleviates the Spectral Bias in Coordinate Networks—0
Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression—0
Binary Probability Model for Learning Based Image Compression—0
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization—0
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model—0
BlinQS: Blind Quality Scalable Image Compression Algorithm without using PCRD Optimization—0
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms—0
Block-optimized Variable Bit Rate Neural Image Compression—0
Breaking the Multi-Enhancement Bottleneck: Domain-Consistent Quality Enhancement for Compressed Images—0
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression—0
C2G-Net: Exploiting Morphological Properties for Image Classification—0
CALLIC: Content Adaptive Learning for Lossless Image Compression—0
Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net—0
Cascade Decoders-Based Autoencoders for Image Reconstruction—0
CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression—0
CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network—0
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression—0
CIC: Circular Image Compression—0
Clustering-Based Quantisation for PDE-Based Image Compression—0
CMamba: Learned Image Compression with State Space Models—0
Coding Standards as Anchors for the CVPR CLIC video track—0
Color Learning for Image Compression—0
Combined Approach for Image Segmentation—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