Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensivel…
Data CompressionLossless SIMD Compression of LiDAR Range and Attribute Scan Sequences
As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited com…
AttributeAutonomous DrivingData CompressionTo Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, a…
Data CompressionEdge-computingBayesian nonparametric estimation of coverage probabilities and distinct counts from sketched data
The estimation of coverage probabilities, and in particular of the missing mass, is a classical statistical problem with applications in numerous scientific fields. In this paper, we study this problem in relation to ran…
Data CompressionConvolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)
In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to rep…
Data CompressionImage Compressionspeech-recognitionSpeech RecognitionA Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing
Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-serv…
Data CompressionEnhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression
Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition (DMD) can extract coherent structures fr…
Data CompressionA Unified Image Preprocessing Framework For Image Compression
With the development of streaming media technology, increasing communication relies on sound and visual information, which puts a massive burden on online media. Data compression becomes increasingly important to reduce …
Data CompressionImage CompressionLossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study
In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, with proof-of-concept application to 4D diffusion-weig…
Data CompressionData centers with quantum random access memory and quantum networks
In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and …
Data CompressionDC-BENCH: Dataset Condensation Benchmark
Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset. As the size of datasets contemporary machine learning models rely o…
AutoMLData AugmentationData CompressionDataset Condensation+1On the Performance of Data Compression in Clustered Fog Radio Access Networks
The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency. However…
Data CompressionA Novel Algorithm for Exact Concave Hull Extraction
Region extraction is necessary in a wide range of applications, from object detection in autonomous driving to analysis of subcellular morphology in cell biology. There exist two main approaches: convex hull extraction, …
Autonomous DrivingData CompressionImage Compressionobject-detection+2RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding
Lidars are depth measuring sensors widely used in autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high costs in data storage and transmission. While lidar data c…
Autonomous DrivingData CompressionImage CompressionAerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines
This paper presents a low-power, self-sustainable, and modular wireless sensor node for aerodynamic and acoustic measurements on wind turbines and other industrial structures. It includes 40 high-accuracy barometers, 10 …
Data CompressionTensor Shape Search for Optimum Data Compression
Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the t…
Data CompressionTensor DecompositionTackling Provably Hard Representative Selection via Graph Neural Networks
Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset. In this paper, we study RS for attributed graphs, and focus on finding representat…
Active LearningData CompressionRepresentation LearningHierarchical Distribution-Aware Testing of Deep Learning
Deep Learning (DL) is increasingly used in safety-critical applications, raising concerns about its reliability. DL suffers from a well-known problem of lacking robustness, especially when faced with adversarial perturba…
Adversarial RobustnessData CompressionDeep LearningAn Efficient Architecture and High-Throughput Implementation of CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA Technology
Nowadays, hyperspectral imaging is recognized as cornerstone remote sensing technology. The explosive growth in image data volume and instrument data rates, compete with limited on-board storage resources and downlink ba…
Data CompressionImage Compression4DAC: Learning Attribute Compression for Dynamic Point Clouds
With the development of the 3D data acquisition facilities, the increasing scale of acquired 3D point clouds poses a challenge to the existing data compression techniques. Although promising performance has been achieved…
AttributeData CompressionMotion CompensationMotion Estimation