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GFSCompNet: remote sensing image compression network based on global feature-assisted segmentation
The proliferation of remote sensing image data in recent years has posed a pressing need for efficient compression techniques due to constrained...
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Attention-based variable-size feature compression module for edge inference
Artificial intelligence has made significant breakthroughs in many fields, especially with the broad deployment of edge devices, which provides...
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Dual-branch spectral–spatial feature extraction network for multispectral image compression
The advent of the information age and the continuous development of spectrum imaging technologies have both triggered an explosion of multispectral...
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A Hybrid Model for Video Compression Based on the Fusion of Feature Compression Framework and Multi-object Tracking Network
This paper proposes a video compression algorithm which bases on the combination of feature compression framework and multi-object tracking network....
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A Single Image High-Perception Super-Resolution Reconstruction Method Based on Multi-layer Feature Fusion Model with Adaptive Compression and Parameter Tuning
We propose a simple image high-perception super-resolution reconstruction method based on multi-layer feature fusion model with adaptive compression...
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Compression-resistant backdoor attack against deep neural networks
In recent years, a number of backdoor attacks against deep neural networks (DNN) have been proposed. In this paper, we reveal that backdoor attacks...
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Structured pruning via feature channels similarity and mutual learning for convolutional neural network compression
The development of convolutional neural network (CNN) have been hindered in resource-constrained devices due to its large memory and calculation. To...
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Compression through extraction of learned parameters from images in de-correlated image space
Image compression is a class of algorithms that reduces the storage space requirement for a digital image. Lossy image compression techniques achieve...
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Mixture autoregressive and spectral attention network for multispectral image compression based on variational autoencoder
Multispectral images, with their unique three-dimensional characteristics, require specialized spatial-spectral feature extraction modules to achieve...
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FANs: fully attentional networks for image compression
We propose a fully attentional networks model and achieve excellent results on top of both large-scale datasets and small-scale datasets to use as a...
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Robust median filtering forensics using texture feature and deep fully connected network
Image forensics researchers have recently focused a lot of attention on detection of median filtering that is used to hide evidence of image forgery...
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Membership inference attacks against compression models
With the rapid development of artificial intelligence, privacy threats are already getting the spotlight. One of the most common privacy threats is...
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Deep learning-assisted medical image compression challenges and opportunities: systematic review
Over the preceding decade, there has been a discernible surge in the prominence of artificial intelligence, marked by the development of various...
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Deep Video Compression Based on 3D Convolution Artifacts Removal and Attention Compression Module
Most research of video compression focuses on how to effectively extract the information between frames and use this information for the subsequent...
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EARN: toward efficient and robust JPEG compression artifact reduction
JPEG is one of the most widely used lossy image compression algorithms, but artifacts are generated during compression. Various artifact reduction...
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Block based learned image compression
Efficient image compression is very important for storage, retrieval, processing and transmission of image contents. The objective is to find a...
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A Complex-Valued Neural Network Based Robust Image Compression
Recent works on learned image compression (LIC) based on convolutional neural networks (CNNs) have achieved great improvement with superior...
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Data Compression Algorithm of Power System
Data compression refers to the use of software performance to improve system utilization without increasing hardware costs. Therefore, in the process...
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2C-Net: integrate image compression and classification via deep neural network
Providing effective support for intelligent vision tasks without image reconstruction can save numerous computational costs in the era of big data....
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FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement
The technology for detecting maritime targets is crucial for realizing ship intelligence. However, traditional detection algorithms are not ideal due...
