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Neural Network Compression
The paper presents the problem of neural network compression, focusing mainly on deep neural networks (DNNs). The negative effects of...
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End-to-End Image Compression Through Machine Semantics
With the increasing demand for AI automated analysis, machine semantics have replaced signals as a new focus in visual information compression. In...
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Metaheuristic-based energy-aware image compression for mobile app development
The widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables...
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AVS Point Cloud Compression Standard
The development of point cloud compression standards has become a necessary method for technological development in emerging fields such as...
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Seismic data compression: an overview
Seismic Data (SD) have been for several decades used as one of the main inspection and exploration tools in various fields, particularly petroleum...
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Divergence Parametric Smoothing in Image Compression Algorithms
AbstractThe paper elaborates on methods of digital image compression. The focus is on the compression method that represents a raster image as a set...
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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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Weighted Burrows–Wheeler Compression
A weight-based dynamic compression method has recently been proposed, which is especially suitable for the encoding of files with locally skewed...
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Towards Clean Reversible Lossless Compression
Zip and unzip are everyday tools in today’s digital world. Since they are inherently inverse to each other, they are ideal for studying reversible...
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Quality controlled 2D ECG compression using adaptive fourier decomposition
This work presents the recently developed Adaptive Fourier Decomposition (AFD) approach-based ECG compression technique that ensures considerable...
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Communication-Efficient Distributed Minimax Optimization via Markov Compression
Recently, the minimax problem has attracted a lot of attention due to its wide applications in modern machine learning fields such as GANs. With the...
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A deep learning based multi-image compression technique
A multi-image compression technique compresses multiple images of the same or various sizes together to generate a common codebook. In multi-image...
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Structure-Based Communication Trace Compression
Communication traces are increasingly important, both for the performance analysis and optimization of parallel applications and for designing...
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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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ECG compression using optimized B-spline
Electrocardiogram (ECG) is widely used in the medical field due to the non-invasive detecting of cardiac anomalies. Transmission, storage, and...
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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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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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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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Optimal Cost Modeling Scheme for Efficient Intra-Prediction Mode in Video Compression
It can be seen that the success rate of the video compression standard H.264 is quite high owing to its wide range of adoption. However, the legacy...
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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...
