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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...
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...

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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

Spatially adaptive subsampling of image sequences.

R F Belfor1, M A Hesp, R L Lagendijk

  • 1Inf. Theory Group, Delft Univ. of Technol.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

Spatially adaptive subsampling improves image sequence coding by adapting sampling lattices to local image content. This method offers better performance than fixed lattice subsampling, especially within motion-compensated coding schemes.

Related Experiment Videos

Last 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

Area of Science:

  • Digital image processing
  • Video compression algorithms
  • Signal processing

Background:

  • Traditional image and video compression often uses fixed sampling lattices.
  • Subsampling can introduce aliasing and reduce image quality if not handled properly.
  • Optimizing sampling strategies is crucial for efficient data compression.

Purpose of the Study:

  • To demonstrate the superiority of spatially adaptive subsampling over fixed lattice subsampling using rate-distortion theory.
  • To introduce a novel algorithm for optimal assignment of sampling lattices in adaptive subsampling.
  • To evaluate the applicability of adaptive subsampling in motion-compensated coding.

Main Methods:

  • Application of rate-distortion theory to analyze subsampling performance.
  • Development of an algorithm for optimal, spatially varying sampling lattice assignment.
  • Integration of the adaptive subsampling method into a motion-compensated video coding framework.

Main Results:

  • Rate-distortion analysis confirms improved performance with spatially adaptive subsampling.
  • Experimental results show significant performance gains compared to fixed lattice subsampling.
  • The proposed method is compatible with existing motion-compensated coding schemes.

Conclusions:

  • Spatially adaptive subsampling provides a more efficient approach to image sequence coding.
  • The developed algorithm enables optimal adaptation of sampling lattices to image content.
  • This technique offers a valuable enhancement for modern video compression standards.