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Related Concept Videos

Downsampling01:20

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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.
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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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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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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...
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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.
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Related Experiment Video

Updated: Apr 26, 2026

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
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Multispectral image compression based on DSC combined with CCSDS-IDC.

Jin Li1, Fei Xing1, Ting Sun1

  • 1Department of Precision Instrument, The State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing 100084, China ; Collaborative Innovation Center for Micro/Nano Fabrication, Device and System, China.

Thescientificworldjournal
|August 12, 2014
PubMed
Summary
This summary is machine-generated.

A new algorithm combines distributed source coding (DSC) with CCSDS image data compression (IDC) for efficient remote sensing multispectral image compression. This method offers improved performance for satellite-based applications with limited resources.

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Area of Science:

  • Remote Sensing
  • Image Compression
  • Data Science

Background:

  • Satellite-based remote sensing requires efficient multispectral image compression due to limited onboard resources (power, memory, processing).
  • Existing 3D transform-based compression algorithms (e.g., 3D DWT, 3D DCT) are too complex for space missions.
  • The Consultative Committee for Space Data Systems (CCSDS) recommends specific image data compression (IDC) approaches.

Purpose of the Study:

  • To develop a low-complexity, high-robustness, and high-performance compression algorithm for remote sensing multispectral images.
  • To address the limitations of existing compression techniques in resource-constrained satellite environments.
  • To leverage distributed source coding (DSC) principles for enhanced compression efficiency.

Main Methods:

  • Sparse representation of each spectral band using Discrete Wavelet Transform (DWT) to obtain wavelet coefficients.
  • Encoding of wavelet coefficients using a Bit Plane Encoder (BPE).
  • Integration of BPE with a Slepian-Wolf (SW) DSC strategy, utilizing Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes for inter-band redundancy removal.

Main Results:

  • The proposed algorithm, termed DSC-CCSDS, demonstrates superior compression performance compared to traditional methods.
  • Experimental validation on a series of multispectral images confirms the algorithm's effectiveness.
  • The deep coupling of BPE and SW-DSC effectively removes residual redundancy between adjacent spectral bands.

Conclusions:

  • The DSC-CCSDS algorithm provides a viable solution for efficient multispectral image compression in space missions.
  • The proposed method achieves a balance of low complexity, high robustness, and high performance.
  • This approach significantly outperforms conventional compression techniques for remote sensing applications.