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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

524
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
524
Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Downsampling01:20

Downsampling

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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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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

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Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
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Related Experiment Video

Updated: May 3, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Hierarchical prediction and context adaptive coding for lossless color image compression.

Seyun Kim, Nam Ik Cho

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 4, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel lossless color image compression algorithm using hierarchical prediction and context-adaptive arithmetic coding. The new method achieves superior bit rate reduction compared to JPEG2000 and JPEG-XR for RGB images.

    Related Experiment Videos

    Last Updated: May 3, 2026

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K

    Area of Science:

    • Computer Science
    • Image Processing
    • Data Compression

    Background:

    • Lossless color image compression is crucial for preserving image fidelity.
    • Existing methods like JPEG2000 and JPEG-XR have limitations in achieving optimal compression ratios.
    • Advanced prediction and coding techniques are needed to improve lossless compression efficiency.

    Purpose of the Study:

    • To develop a new lossless color image compression algorithm.
    • To improve upon existing compression standards by reducing bit rates.
    • To enhance prediction accuracy in lossless image compression.

    Main Methods:

    • Decorrelation of RGB images using a reversible color transform.
    • Hierarchical prediction scheme for chrominance components using upper, left, and lower pixels.
    • Context-adaptive arithmetic coding applied to prediction errors.

    Main Results:

    • The proposed algorithm achieves lower bit rates than JPEG2000 and JPEG-XR.
    • Hierarchical prediction enhances compression efficiency for chrominance channels.
    • Context-adaptive arithmetic coding effectively models prediction errors.

    Conclusions:

    • The developed algorithm offers a significant improvement in lossless color image compression.
    • The hierarchical prediction and context-adaptive coding approach is effective for reducing bit rates.
    • This method presents a competitive alternative for high-fidelity image compression.