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 Theorem01:15

Sampling Theorem

1.1K
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.
1.1K
Thevinin's Theorem01:15

Thevinin's Theorem

1.2K
Thévenin's theorem plays a pivotal role in electrical circuit analysis, offering a solution to the challenges posed by variable loads within a circuit. In practical applications, it is common to encounter circuits where certain elements remain fixed while others fluctuate, often referred to as the "load." A typical household electrical outlet serves as a prime example of a variable load, as it can be connected to a variety of appliances, each with its own unique electrical characteristics.
1.2K
Even and Odd Signals01:17

Even and Odd Signals

1.8K
An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
1.8K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

43
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
43
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.5K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.5K
Encoding01:19

Encoding

596
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
596

You might also read

Related Articles

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

Sort by
Same author

Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels.

Entropy (Basel, Switzerland)·2021
Same author

Information Theoretic Security for Shannon Cipher System under Side-Channel Attacks <sup>†</sup>.

Entropy (Basel, Switzerland)·2020
Same author

A Direct Link between Rényi-Tsallis Entropy and Hölder's Inequality-Yet Another Proof of Rényi-Tsallis Entropy Maximization.

Entropy (Basel, Switzerland)·2020
Same author

Exponential Strong Converse for One Helper Source Coding Problem.

Entropy (Basel, Switzerland)·2020
Same author

Information Theoretic Security for Broadcasting of Two Encrypted Sources under Side-Channel Attacks <sup>†</sup>.

Entropy (Basel, Switzerland)·2020

Related Experiment Video

Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.4K

Exponential Strong Converse for Source Coding with Side Information at the Decoder.

Yasutada Oohama1

  • 1Department of Communication Engineering and Informatics, University of Electro-Communications, Tokyo 182-8585, Japan.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study analyzes data compression error probabilities outside the Wyner-Ziv rate distortion region. We show these error probabilities decrease exponentially, establishing a strong converse coding theorem for Wyner-Ziv source coding.

Keywords:
exponent function outside the rate distortion regionsource coding with side information at the decoderstrong converse theoremthe rate distortion region

More Related Videos

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.0K

Related Experiment Videos

Last Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.4K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.0K

Area of Science:

  • Information Theory
  • Data Compression
  • Coding Theory

Background:

  • The Wyner-Ziv problem addresses data compression with side information available at the decoder.
  • The rate-distortion region defines achievable trade-offs between compression rate (R) and distortion level (Δ).

Purpose of the Study:

  • To investigate the error probability of decoding for (R, Δ) pairs outside the established Wyner-Ziv rate-distortion region.
  • To analyze the performance of Wyner-Ziv source coding when operating beyond theoretical limits.

Main Methods:

  • Mathematical analysis of decoding error probabilities.
  • Derivation of exponential bounds on error probability.
  • Proof of the strong converse coding theorem as a corollary.

Main Results:

  • Demonstrated that decoding error probability approaches zero exponentially for (R, Δ) outside the rate-distortion region.
  • Derived an explicit lower bound for the exponent of this probability.
  • Established the strong converse coding theorem for the Wyner-Ziv problem.

Conclusions:

  • The Wyner-Ziv source coding problem exhibits a strong converse property.
  • Performance degrades gracefully but predictably when operating outside the optimal rate-distortion region.