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Related Concept Videos

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...
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...
Deconvolution01:20

Deconvolution

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.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients 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...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Convergence of Fourier Series01:21

Convergence of Fourier Series

The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...

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Related Experiment Video

Updated: Jul 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Adaptive polyphase subband decomposition structures for image compression.

O N Gerek1, A E Cetin

  • 1Dept. of Electr. and Electron. Eng., Anadola Univ., Eskisehir, Turkey. ongerek@anadolu.edu.tr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 12, 2008
PubMed
Summary

Adaptive filter banks improve image compression by adjusting filters for signals with varying characteristics like text or edges. This overcomes artifacts from standard subband decomposition methods.

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Area of Science:

  • Signal Processing
  • Image Analysis
  • Data Compression

Background:

  • Subband decomposition is common for data coding and analysis, aiming to separate signals into spectral regions.
  • Standard filter banks create artifacts in images with spatially varying features like text or sharp edges.

Purpose of the Study:

  • To introduce adaptive filter banks with perfect reconstruction for images exhibiting spatially varying characteristics.
  • To enhance image compression ratios by addressing artifacts caused by traditional decomposition methods.

Main Methods:

  • Developed adaptive filter banks where linear or nonlinear filters adjust based on signal properties.
  • Ensured the filter banks possess the perfect reconstruction property for accurate signal recovery.

Main Results:

  • Adaptive filter banks effectively reduce artifacts in images with text, subtitles, and sharp edges.
  • Achieved improved image compression ratios compared to conventional subband decomposition techniques.

Conclusions:

  • Adaptive filter banks offer a superior solution for processing images with complex, spatially varying content.
  • The proposed method enhances both the quality and efficiency of image compression for challenging datasets.