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Related Concept Videos

Histogram01:05

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Transformations of Functions II01:29

Transformations of Functions II

Transformations in mathematics alter the position or orientation of a function’s graph while preserving its fundamental shape. One important type of transformation is the horizontal shift, which involves modifying the input variable within a function’s equation. This operation affects where outputs occur along the horizontal axis but does not alter the function’s overall structure.A horizontal shift is achieved by replacing the input variable x with either x + c or x - c, where c is a constant.
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  1. Home
  3. General Framework To Histogram-shifting-based Reversible Data Hiding.
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  3. General Framework To Histogram-shifting-based Reversible Data Hiding.

Related Experiment Video

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

General framework to histogram-shifting-based reversible data hiding.

Xiaolong Li1, Bin Li, Bin Yang

  • 1Institute of Computer Science and Technology, Peking University, Beijing 100871, China. lixiaolong@pku.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2013

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a general framework for reversible data hiding (RDH) using histogram shifting (HS). The framework simplifies the creation of efficient HS-based RDH algorithms, offering high capacity and low distortion.

Related Experiment Videos

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

  • Computer Science
  • Information Security
  • Data Compression

Background:

  • Reversible data hiding (RDH) is crucial for embedding information discreetly.
  • Histogram shifting (HS) is an established RDH technique known for efficiency.
  • Existing HS-based RDH methods offer high capacity and low distortion.

Purpose of the Study:

  • To present a general framework for constructing histogram shifting-based reversible data hiding algorithms.
  • To demonstrate the universality and applicability of the proposed framework.
  • To introduce novel and efficient RDH algorithms derived from the framework.

Main Methods:

  • Developed a general framework for HS-based RDH.
  • Defined 'shifting' and 'embedding' functions as key components.
  • Showcased existing RDH algorithms as special cases of the framework.
  • Introduced two new HS-based RDH algorithms.

    • Main Results:

    • The proposed framework unifies various HS-based RDH algorithms.
    • New algorithms demonstrate the framework's effectiveness and efficiency.
    • The framework allows for the design of RDH algorithms with high capacity and low distortion.

    Conclusions:

    • The general framework simplifies the development of HS-based RDH algorithms.
    • The framework is versatile, encompassing existing and novel approaches.
    • Future research can leverage this framework to devise more advanced RDH techniques.