Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Building internationally competitive journals for science data sharing: The evolution of three biomedical English academic journals launched in China.

Biosafety and health·2026
Same author

LatinVisco: A latin american expert consensus on viscosupplementation for knee osteoarthritis.

Complementary therapies in medicine·2026
Same author

Sleep facilitates pattern separation through SK channel-mediated sparse coding.

Current biology : CB·2026
Same author

Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila.

Nature communications·2026
Same author

Mega-plication is a novel gastric remodeling procedure for weight loss.

VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy·2025
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Sampling-based correlation estimation for distributed source coding under rate and complexity constraints.

Ngai-Man Cheung1, Huisheng Wang, Antonio Ortega

  • 1Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. n.man.cheung@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 1, 2008
PubMed
Summary
This summary is machine-generated.

Accurate correlation estimation is key for efficient distributed source coding (DSC). This study models estimation error impact on coding efficiency for binary and continuous data, optimizing sample allocation for minimized rate penalty in image and video compression.

Related Experiment Videos

Last Updated: Jun 28, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

  • Data Compression
  • Information Theory
  • Signal Processing

Background:

  • Distributed Source Coding (DSC) relies on accurate correlation estimation for optimal encoding rates.
  • The impact of estimation errors on DSC efficiency, particularly in practical image and video applications, remains under-explored.
  • Existing methods often overlook the trade-offs between estimation accuracy, coding rate, and computational complexity.

Purpose of the Study:

  • To investigate correlation estimation under rate and complexity constraints within a DSC framework.
  • To develop models and algorithms for minimizing coding rate penalties due to estimation errors in binary and continuous data compression.
  • To enhance Slepian-Wolf coding and sampling techniques for practical distributed image and video compression.

Main Methods:

  • Developed a model characterizing the relationship between sample count and coding rate penalty for single and multiple binary sources.
  • Proposed an algorithm for optimal sample allocation to minimize overall rate penalty under a total sample constraint.
  • Introduced a model-based estimation approach for continuous-valued data, deriving bit-plane statistics analytically from estimated source and correlation models.
  • Extended model-based estimation to significance-based bit-plane extraction, relevant for wavelet-based applications.

Main Results:

  • The proposed model effectively characterizes the rate penalty associated with correlation estimation in binary data compression.
  • The sample allocation algorithm successfully minimizes the overall rate penalty for multiple binary sources under constraints.
  • Model-based estimation demonstrated effectiveness in deriving bit-plane statistics for continuous data DSC, validated by hyperspectral image compression experiments.
  • The algorithms show practical utility in optimizing distributed image and video compression.

Conclusions:

  • Accurate correlation estimation is crucial for achieving high coding efficiency in DSC applications.
  • The developed models and algorithms provide practical solutions for managing estimation errors and optimizing resource allocation (e.g., samples) in DSC.
  • The findings offer significant improvements for distributed image and video compression, particularly in scenarios involving binary data and bit-plane extraction.