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Related Concept Videos

Probability Histograms01:17

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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 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).
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The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures.
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Three-dimensional (3D) phase-resolved functional lung (PREFUL) is a functional magnetic resonance imaging (MRI) technique that allows for quantification of regional ventilation of the whole human lung volume, using tidal breathing and contrast-agent free acquisition for 8 min. Here, we present an MR protocol to collect and analyze 3D PREFUL imaging...
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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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Related Experiment Video

Updated: Jan 19, 2026

Production of Membrane-Filtered Phase-Shift Decafluorobutane Nanodroplets from Preformed Microbubbles
07:10

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High-capacity reversible data hiding in encrypted images based on two-phase histogram shifting.

Kai Meng Chen1, Chin-Chen Chang2

  • 1Computer Engineering College, Jimei University, Xiamen, 361021, China.

Mathematical Biosciences and Engineering : MBE
|September 11, 2019
PubMed
Summary

This study introduces a novel reversible data hiding technique for encrypted images using two-phase histogram shifting. The method enhances embedding capacity while preserving image quality and content security in cloud environments.

Keywords:
additive homomorphismhigh capacityhistogram shiftingimage encryptionreversible data hiding

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Area of Science:

  • Computer Science
  • Information Security
  • Digital Image Processing

Background:

  • Cloud services necessitate secure management of encrypted private data.
  • Encrypted domain signal processing is crucial for privacy-preserving data handling.
  • Existing methods struggle to balance embedding capacity, security, and reversibility.

Purpose of the Study:

  • To propose a new reversible data hiding method for encrypted images.
  • To enhance data embedding capacity while maintaining content security.
  • To ensure perfect image recovery after data extraction.

Main Methods:

  • Image encryption using special division and additive homomorphic encryption.
  • Leveraging partial spatial correlation in the encrypted image for data embedding.
  • Employing a two-phase histogram shifting scheme on difference histograms for data embedding.

Main Results:

  • The proposed method achieves high embedding capacity by utilizing two difference histograms.
  • Content security is maintained throughout the data hiding process.
  • Experimental results show improved embedding capacity compared to related methods.
  • The visual quality of the marked encrypted image is preserved.

Conclusions:

  • The novel reversible data hiding method offers efficient and secure data embedding in encrypted images.
  • The technique effectively balances embedding capacity, security, and reversibility for cloud applications.
  • This approach provides a robust solution for privacy-preserving data management in encrypted domains.