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Related Concept Videos

Transformations of Functions III01:20

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
Basic Operations on Signals01:22

Basic Operations on Signals

Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
Properties of DTFT I01:24

Properties of DTFT I

In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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Related Experiment Video

Updated: May 30, 2026

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Low distortion transform for reversible watermarking.

Dinu Coltuc

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 22, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel low-distortion transform for reversible watermarking, enhancing data security with minimal image quality loss. The new method outperforms existing techniques, particularly with complex predictors, ensuring robust and imperceptible digital watermarking.

    Related Experiment Videos

    Last Updated: May 30, 2026

    Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
    07:03

    Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

    Published on: February 23, 2017

    Area of Science:

    • Digital image processing
    • Information security
    • Data embedding techniques

    Background:

    • Reversible watermarking allows data extraction without image degradation.
    • Traditional prediction-error expansion methods can introduce significant distortion.
    • Minimizing distortion is crucial for practical watermarking applications.

    Discussion:

    • The proposed transform embeds expanded prediction errors into both current pixels and their context.
    • This approach minimizes the squared error introduced by watermarking.
    • It offers improved performance over classical methods with complex predictors like MED and GAP.

    Key Insights:

    • A novel low-distortion transform for prediction-error expansion reversible watermarking is presented.
    • The transform achieves superior performance compared to existing methods, especially for complex predictors.
    • Experimental validation confirms the effectiveness and reduced distortion of the proposed approach.

    Outlook:

    • Further research can explore adaptive predictor selection for enhanced performance.
    • The transform's applicability to other watermarking schemes warrants investigation.
    • Real-world implementation and testing in various image formats are recommended.