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Related Concept Videos

Upsampling01:22

Upsampling

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...
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...
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...
Reducing Line Loss01:18

Reducing Line Loss

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 in...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

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Related Experiment Video

Updated: May 23, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Rate-distortion optimised video transmission using pyramid vector quantisation.

Syed Bokhari, Andrew R Nix, David R Bull

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 29, 2012
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an efficient algorithm for Pyramid Vector Quantisation (PVQ) video compression. PVQ offers high error resilience and similar compression to H.264/AVC, reducing the need for complex error correction in wireless environments.

    Related Experiment Videos

    Last Updated: May 23, 2026

    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    Area of Science:

    • Digital video compression
    • Information theory
    • Error resilient coding

    Background:

    • Conventional video compression methods (e.g., H.264/AVC) achieve high compression ratios using interframe and intra frame prediction, but are sensitive to channel errors.
    • Error propagation in conventional codecs necessitates complex error detection and correction mechanisms for reliable transmission over lossy channels.
    • Pyramid Vector Quantisation (PVQ) offers inherent error resilience through fixed-length codewords, preventing error propagation.

    Purpose of the Study:

    • To introduce an efficient rate-distortion optimization algorithm for intra-mode PVQ.
    • To evaluate the compression performance and error resilience of the enhanced PVQ codec.
    • To compare the proposed method against state-of-the-art codecs in a realistic wireless environment.

    Main Methods:

    • Development of an efficient rate-distortion optimization algorithm for intra-mode PVQ.
    • Implementation and evaluation of the enhanced PVQ codec.
    • Testing with High Definition (HD) content over a simulated IEEE 802.11n wireless network.

    Main Results:

    • The intra-mode PVQ algorithm achieves compression performance comparable to intra H.264/AVC and Motion JPEG 2000.
    • The PVQ codec demonstrates inherent error resilience, providing high tolerance to corrupted data.
    • The proposed method obviates the need for complex encoding tools typically required for error correction.

    Conclusions:

    • The enhanced intra-mode PVQ algorithm offers a compelling alternative for video compression, balancing compression efficiency with robust error resilience.
    • PVQ-based video coding is highly suitable for lossy wireless environments, simplifying transmission requirements.
    • This approach significantly improves data integrity without compromising video quality or increasing encoding complexity.