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Related Concept Videos

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
587
Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Discrete-time Fourier transform01:26

Discrete-time Fourier transform

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
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Properties of the z-Transform I01:17

Properties of the z-Transform I

556
The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
556
Fast Fourier Transform01:10

Fast Fourier Transform

792
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
792
Properties of DTFT I01:24

Properties of DTFT I

685
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...
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Related Experiment Video

Updated: Dec 26, 2025

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Steerable-Discrete-Cosine-Transform (SDCT): Hardware Implementation and Performance Analysis.

Riccardo Peloso1, Maurizio Capra1, Luigi Sole1

  • 1Department of Electronics and Telecommunication (DET), Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy.

Sensors (Basel, Switzerland)
|March 8, 2020
PubMed
Summary
This summary is machine-generated.

A new hardware architecture for the Steerable Discrete Cosine Transform (SDCT) enhances video compression. This innovation improves coding efficiency for High Efficiency Video Coding (HEVC) and supports 8k UltraHigh Definition content.

Keywords:
VLSIdirectional transformdiscrete cosine transformvideo coding

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Area of Science:

  • Digital signal processing
  • Video compression technologies
  • Hardware architecture design

Background:

  • The increasing demand for efficient video compression methods is driven by rising frame resolutions.
  • The H.265/High Efficiency Video Coding (HEVC) standard is the current state-of-the-art in video coding.
  • Existing methods require further improvements in coding efficiency.

Purpose of the Study:

  • To propose a novel hardware architecture for the Steerable Discrete Cosine Transform (SDCT).
  • To evaluate the performance of the SDCT within the HEVC standard for enhanced video compression.
  • To enable support for high-resolution video formats like 8k UHD.

Main Methods:

  • Implementation of a hardware architecture for the Steerable Discrete Cosine Transform (SDCT).
  • Integration of SDCT capabilities into the High Efficiency Video Coding (HEVC) framework.
  • Performance evaluation at high frequencies and throughputs.

Main Results:

  • The developed SDCT hardware architecture operates at 188 MHz with a throughput of 3.00 GSample/s.
  • The architecture supports 8k UltraHigh Definition (7680 × 4320) video at 60 Hz.
  • Preliminary results indicate improved compression ratios when SDCT is embedded in HEVC.

Conclusions:

  • The proposed SDCT hardware architecture offers significant advancements in video compression efficiency.
  • This architecture effectively supports demanding 8k UHD video requirements within the HEVC standard.
  • The SDCT approach demonstrates potential for superior compression performance in future video coding standards.