Image Compression
226 papers with code • 11 benchmarks • 11 datasets
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
Benchmarks
These leaderboards are used to track progress in Image Compression
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Libraries
Use these libraries to find Image Compression models and implementationsDatasets
CIFAR-10
STL-10
Food-101
BSDS500
FER2013
ImageNet-32
PCam
Oxford-IIIT Pet Dataset
Oxford-IIIT Pets
Subtasks
Latest papers
Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs
We compare our approach to a similar CUDA implementation for MLPs and show that our implementation on the Intel Data Center GPU outperforms the CUDA implementation on Nvidia's H100 GPU by a factor up to 2. 84 in inference and 1. 75 in training.
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead.
S2LIC: Learned Image Compression with the SwinV2 Block, Adaptive Channel-wise and Global-inter Attention Context
wyq2021/s2lic
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In this paper, we propose an Adaptive Channel-wise and Global-inter attention Context (ACGC) entropy model, which can efficiently achieve dual feature aggregation in both inter-slice and intraslice contexts.
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Tencent/TFace
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Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.
Overfitted image coding at reduced complexity
orange-opensource/cool-chic
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Such codecs include Cool-chic, which presents image coding performance on par with VVC while requiring around 2000 multiplications per decoded pixel.
Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000
The proposed approach involves two steps, a high-to-low step followed by a low-to-high step.
Region-Adaptive Transform with Segmentation Prior for Image Compression
GityuxiLiu/Region-Adaptive-Transform-with-Segmentation-Prior-for-Image-Compression
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However, there is no prior research on neural transform that focuses on specific regions.
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets
InterDigitalInc/CompressAI
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Third, variable rate quantization is used also for the hyper latent.
Towards Backward-Compatible Continual Learning of Image Compression
viper-purdue/continual-compression
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This paper explores the possibility of extending the capability of pre-trained neural image compressors (e. g., adapting to new data or target bitrates) without breaking backward compatibility, the ability to decode bitstreams encoded by the original model.
MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model
With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic.
