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 401–450 of 1008 papers

TitleStatusHype
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Enhancing Perception Quality in Remote Sensing Image Compression via Invertible Neural Network—0
Full Quaternion Representation of Color images: A Case Study on QSVD-based Color Image Compression—0
Compact Latent Representation for Image Compression (CLRIC)—0
A Generative Model of Natural Texture Surrogates—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Convolutional Transformer-Based Image Compression—0
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting—0
Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP—0
GAN-based Image Compression with Improved RDO Process—0
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization—0
GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation—0
Gated Context Model with Embedded Priors for Deep Image Compression—0
Enhancement of theColor Image Compression Using a New Algorithm based on Discrete Hermite Wavelet Transform—0
Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder—0
An Unsupervised Information-Theoretic Perceptual Quality Metric—0
Human Synthesis and Scene Compositing—0
Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks—0
Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution—0
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling—0
Generative Latent Coding for Ultra-Low Bitrate Image Compression—0
Enhanced Residual SwinV2 Transformer for Learned Image Compression—0
GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression—0
Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images—0
Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion—0
GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform—0
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index—0
Image Processing via Multilayer Graph Spectra—0
Graph Spectral Image Processing—0
Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression—0
GroupedMixer: An Entropy Model with Group-wise Token-Mixers for Learned Image Compression—0
Guided Diffusion for the Extension of Machine Vision to Human Visual Perception—0
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning—0
Bridging Compressed Image Latents and Multimodal Large Language Models—0
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates—0
DCT Approximations Based on Chen's Factorization—0
Efficient Pruning for Machine Learning Under Homomorphic Encryption—0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Image Coding for Machines with Object Region Learning—0
Saliency Driven Perceptual Image Compression—0
Hierarchical Image Compression Framework—0
Image Compression By Embedding Five Modulus Method Into JPEG—0
High Efficiency Image Compression for Large Visual-Language Models—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Image Compression using only Attention based Neural Networks—0
Image-GS: Content-Adaptive Image Representation via 2D Gaussians—0
A GAN-based Tunable Image Compression System—0
End-to-End Optimized Image Compression with the Frequency-Oriented Transform—0
HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods—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