Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations
Joint Photographic Experts Group (JPEG) achieves data compression by quantizing Discrete Cosine Transform (DCT) coefficients, which inevitably introduces compression artifacts. Most existing JPEG quality enhancement meth…
Data CompressionGratNet: A Photorealistic Neural Shader for Diffractive Surfaces
Structural coloration is commonly modeled using wave optics for reliable and photorealistic rendering of natural, quasi-periodic and complex nanostructures. Such models often rely on dense, preliminary or preprocessed da…
Data CompressionSSIMAstroCompress: A benchmark dataset for multi-purpose compression of astronomical data
The site conditions that make astronomical observatories in space and on the ground so desirable -- cold and dark -- demand a physical remoteness that leads to limited data transmission capabilities. Such transmission li…
Data CompressionKernel $k$-Medoids as General Vector Quantization
Vector Quantization (VQ) is a widely used technique in machine learning and data compression, valued for its simplicity and interpretability. Among hard VQ methods, $k$-medoids clustering and Kernel Density Estimation (K…
Data CompressionDensity EstimationDiversityQuantizationFourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression
Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatial resolutions across multiple channels p…
Data CompressionImage CompressionStationary MMD Points for Cubature
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance, arising in cubature, data compression, and optimisation. Several authors have proposed to select poi…
Data CompressionMiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existing LCU benchmarks for LLMs often result …
Data CompressionLong-Context UnderstandingInformation-theoretic Generalization Analysis for VQ-VAEs: A Role of Latent Variables
Latent variables (LVs) play a crucial role in encoder-decoder models by enabling effective data compression, prediction, and generation. Although their theoretical properties, such as generalization, have been extensivel…
Data CompressionDecoderEfficient compression of neural networks and datasets
We compare, improve, and contribute methods that substantially decrease the number of parameters of neural networks while maintaining high test accuracy. When applying our methods to minimize description length, we obtai…
Data CompressionLearning novel representations of variable sources from multi-modal $\textit{Gaia}$ data via autoencoders
Gaia Data Release 3 (DR3) published for the first time epoch photometry, BP/RP (XP) low-resolution mean spectra, and supervised classification results for millions of variable sources. This extensive dataset offers a uni…
Data CompressionOutlier DetectionCombining Progressive Image Compression and Random Access in DNA Data Storage
The exponential increase in storage demand and low lifespan of data storage devices has resulted in long-term archival and preservation emerging as a critical bottlenecks in data storage. In order to meet this demand, re…
Data CompressionImage CompressionDD-Ranking: Rethinking the Evaluation of Dataset Distillation
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the origina…
Data AugmentationData CompressionDataset DistillationIs Compression Really Linear with Code Intelligence?
Understanding the relationship between data compression and the capabilities of Large Language Models (LLMs) is crucial, especially in specialized domains like code intelligence. Prior work posited a linear relationship …
Data CompressionClustering-based Low-Rank Matrix Approximation: An Adaptive Theoretical Analysis with Application to Data Compression
Low-rank matrix approximation (LoRMA) is a fundamental tool for compressing high-resolution data matrices by extracting important features while suppressing redundancy. Low-rank methods, such as global singular value dec…
Data CompressionDiagnosticSSIMTowards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT) environments introduces specific challe…
Anomaly DetectionData CompressionReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection
Continual Anomaly Detection (CAD) enables anomaly detection models in learning new classes while preserving knowledge of historical classes. CAD faces two key challenges: catastrophic forgetting and segmentation of small…
Anomaly Detectioncontinual anomaly detectionData CompressionSegmentationEvent-based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems
Implantable brain-machine interfaces (iBMIs) are evolving to record from thousands of neurons wirelessly but face challenges in data bandwidth, power consumption, and implant size. We propose a novel Spiking Neural Netwo…
Data Compression3D Gaussian Splatting Data Compression with Mixture of Priors
3D Gaussian Splatting (3DGS) data compression is crucial for enabling efficient storage and transmission in 3D scene modeling. However, its development remains limited due to inadequate entropy models and suboptimal quan…
3DGSData CompressionMixture-of-ExpertsQuantizationHybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Most existing 3D Gaussian Splatting (3DGS) compression schemes focus on producing compact 3DGS representation via implicit data embedding. They have long coding times and highly customized data format, making it difficul…
3DGSData CompressionConvolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies
Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition syste…
Anomaly DetectionData Compression