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
DRASIC: Distributed Recurrent Autoencoder for Scalable Image CompressionCode0
Generative Adversarial Networks for Extreme Learned Image CompressionCode0
HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly DetectionCode0
Image Compression with Encoder-Decoder Matched Semantic SegmentationCode0
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive AutoencodersCode0
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Fractal image compression using upper bound on scaling parameterCode0
GABIC: Graph-based Attention Block for Image CompressionCode0
Learning-Based Image Compression for MachinesCode0
Breaking the Barriers of One-to-One Usage of Implicit Neural Representation in Image Compression: A Linear Combination Approach with Performance GuaranteesCode0
Fine-grained subjective visual quality assessment for high-fidelity compressed imagesCode0
Five Modulus Method For Image CompressionCode0
FCNR: Fast Compressive Neural Representation of Visualization ImagesCode0
An Underparametrized Deep Decoder Architecture for Graph SignalsCode0
End-to-end optimized image compression with competition of prior distributionsCode0
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial ExamplesCode0
Energy-Aware JPEG Image Compression: A Multi-Objective ApproachCode0
Flexible Mixed Precision Quantization for Learned Image CompressionCode0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
Image Compression With Learned Lifting-Based DWT and Learned Tree-Based Entropy ModelsCode0
MALICE: Manipulation Attacks on Learned Image ComprEssion—0
Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression—0
Deformation Aware Image Compression—0
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms—0
Deep Stereo Image Compression via Bi-Directional Coding—0
Deep Spatial and Tonal Data Optimisation for Homogeneous Diffusion Inpainting—0
BlinQS: Blind Quality Scalable Image Compression Algorithm without using PCRD Optimization—0
DeepSIC: Deep Semantic Image Compression—0
Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria—0
An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems—0
Adapting JPEG XS gains and priorities to tasks and contents—0
Deep Sampling Networks—0
Deep Probabilistic Video Compression—0
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model—0
Deep Generative Video Compression—0
Deep Predictive Video Compression with Bi-directional Prediction—0
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization—0
Deep Perceptual Image Quality Assessment for Compression—0
Deep Perceptual Compression—0
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework—0
Deep Networks for Compressed Image Sensing—0
Binary Probability Model for Learning Based Image Compression—0
An Efficient Architecture and High-Throughput Implementation of CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA Technology—0
Adaptation and Attention for Neural Video Coding—0
A bi-level view of inpainting - based image compression—0
Deep Lossless Image Compression via Masked Sampling and Coarse-to-Fine Auto-Regression—0
Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression—0
Deep Learning-based Image Compression with Trellis Coded Quantization—0
An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks—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