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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

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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...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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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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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
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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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Convolution computations can be simplified by utilizing their inherent properties.
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Related Experiment Video

Updated: Dec 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

926

Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding.

Jan P Klopp, Liang-Gee Chen, Shao-Yi Chien

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 10, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel convolutional neural network (CNN) approach for video compression, achieving significant coding gains with minimal computational cost. The method efficiently exploits video data redundancies, enhancing storage and transmission efficiency for digital media.

    Related Experiment Videos

    Last Updated: Dec 22, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

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    Published on: December 15, 2023

    926

    Area of Science:

    • Computer Vision
    • Digital Signal Processing
    • Machine Learning

    Background:

    • Digital media, especially video, is rapidly increasing, demanding advanced compression techniques.
    • Conventional video codecs struggle to exploit non-local redundancies effectively.
    • Efficient storage and transmission are critical for ubiquitous digital media.

    Purpose of the Study:

    • To develop a video compression method that exploits non-local redundancies.
    • To design convolutional neural networks (CNNs) with low memory and computational footprint.
    • To improve coding gains for existing video codecs.

    Main Methods:

    • Designing CNNs optimized for low computational complexity.
    • Training network parameters on-the-fly at encoding time to predict residual signals.
    • Compressing and signalling trained network parameters within the video codec.

    Main Results:

    • Achieved coding gains comparable to pretrained denoising CNNs.
    • Required only 1% of the computational complexity of existing CNN-based methods.
    • Demonstrated stable performance on long video segments (up to 256 frames).

    Conclusions:

    • The proposed CNN approach offers significant coding gains for video compression.
    • The method is applicable to any existing video codec, enhancing efficiency.
    • Its low computational footprint enables application in resource-constrained environments.