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Related Concept Videos

Upsampling01:22

Upsampling

303
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...
303
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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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Aliasing01:18

Aliasing

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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...
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Downsampling01:20

Downsampling

245
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...
245
Bandpass Sampling01:17

Bandpass Sampling

253
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....
253
Sampling Theorem01:15

Sampling Theorem

744
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization.

Qunlin Chen1, Derong Chen1, Jiulu Gong1

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

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|July 9, 2022
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Summary

This study introduces a computationally efficient adaptive sampling method for block compressed sensing (BCS). The new approach minimizes distortion, significantly improving rate-distortion performance with minimal added complexity for resource-constrained applications.

Keywords:
adaptive samplingblock compressed sensingdistortion minimizationdistortion modelimage samplingneural network

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Area of Science:

  • Signal Processing
  • Image Compression
  • Computer Vision

Background:

  • Block Compressed Sensing (BCS) is valuable for image sampling and compression in resource-limited scenarios.
  • Adaptive sampling enhances BCS rate-distortion performance but increases encoder complexity, negating BCS advantages.
  • Existing methods face high computational overhead, limiting practical application.

Purpose of the Study:

  • To develop an adaptive sampling method for BCS with low computational complexity.
  • To improve the rate-distortion performance of BCS without sacrificing efficiency.
  • To address the high computational cost associated with current adaptive sampling techniques.

Main Methods:

  • Analyzed computational complexity of existing adaptive sampling methods for BCS.
  • Modeled adaptive sampling as a distortion minimization problem.
  • Developed three distortion models relating block sampling rate to block distortion.
  • Utilized a neural network for predicting distortion model parameters.
  • Proposed a fast estimation method for block sampling rate allocation.

Main Results:

  • The proposed fast estimation method effectively allocates block sampling rates.
  • Two of the three distortion models yielded superior performance compared to existing adaptive methods.
  • The additional computational cost of the proposed method is less than 1.9% compared to standard BCS at a 0.1 sampling rate.

Conclusions:

  • The developed method achieves improved adaptive sampling performance for BCS with low computational complexity.
  • The distortion minimization approach and fast estimation provide a practical solution for resource-constrained applications.
  • This research offers a viable alternative to computationally intensive adaptive sampling techniques in BCS.