Jove
Visualize
Contact Us

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...
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...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Assessment of Clinical and Neurological Alterations Before Radiation Therapy in Children With Malignant Brain Tumours.

Clinical oncology (Royal College of Radiologists (Great Britain))·2025
Same author

Biological and therapeutic implications of FGFR alterations in urothelial cancer: A systematic review from non-muscle-invasive to metastatic disease.

Actas urologicas espanolas·2025
Same author

Predicting value for incomplete recovery in Bell's palsy of facial nerve ultrasound versus nerve conduction study.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2023
Same author

NG2-glia: rising stars in stress-related mental disorders?

Molecular psychiatry·2022
Same author

Preventing colour fading in artworks with graphene veils.

Nature nanotechnology·2021
Same author

Detection of acidic paper recovery after alkaline nanoparticle treatment by 2D NMR relaxometry.

Magnetic resonance in chemistry : MRC·2020
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 7, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization.

C D'Elia1, G Poggi, L Verdoliva

  • 1Dipartimento di Ingegneria Elettronica, Naples Univ. c.delia@unina.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces trellis-coded vector quantization (TCVQ) for compressing Synthetic Aperture Radar (SAR) data. TCVQ offers comparable performance to previous methods but with significantly reduced complexity, enabling on-board implementation for spaceborne sensors.

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Related Experiment Videos

Last Updated: Jul 7, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Compression

Background:

  • Synthetic Aperture Radar (SAR) data require substantial storage and transmission resources, particularly for spaceborne sensors with limited downlink capacity.
  • Standard compression algorithms are less effective on raw SAR data due to a lack of exploitable properties, as these properties emerge only after complex focusing processes.
  • Previous work by Poggi et al. (2000) utilized low-complexity range focusing to enhance SAR data correlation and energy concentration for compression.

Purpose of the Study:

  • To develop a more computationally efficient compression technique for SAR data suitable for on-board implementation.
  • To replace complex Vector Quantization (VQ) with a less resource-intensive method while maintaining compression performance.
  • To investigate the effectiveness of trellis-coded VQ (TCVQ) for SAR data compression.

Main Methods:

  • Implemented a low-complexity range focusing on SAR data to improve correlation and energy concentration.
  • Replaced Vector Quantization (VQ) with trellis-coded VQ (TCVQ) to reduce computational complexity.
  • Utilized small vectors within the TCVQ framework to manage complexity, while leveraging trellis coding for efficient encoding of larger data blocks.

Main Results:

  • The proposed TCVQ method achieved compression performance comparable to the earlier VQ-based approach.
  • The computational complexity of TCVQ was significantly lower than VQ, making it feasible for on-board processing.
  • Experiments on real SAR data demonstrated the practical viability of TCVQ for spaceborne applications.

Conclusions:

  • Trellis-coded VQ presents a viable and efficient solution for compressing SAR data on spaceborne platforms.
  • The reduced complexity of TCVQ facilitates on-board implementation, addressing the limitations of current SAR data transmission.
  • This approach offers a practical improvement over existing SAR data compression techniques, balancing performance and computational cost.