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Upsampling

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...

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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

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An Adaptive Rate Blocked Compressive Sensing Method for Video.

Jianming Wang¹, Jianhua Chen¹

¹School of Information Science and Engineering, Yunnan University, Kunming 650500, China.

Entropy (Basel, Switzerland)

|August 27, 2021

Summary

This summary is machine-generated.

This study introduces an adaptive rate Compressive Sensing (CS) method for video signals, improving sampling efficiency. The novel approach enhances reconstructed video quality using block-based sparsity estimation and adaptive sampling rates.

Keywords:

adaptive rate sampling compressive sensing sparsity estimation statistical parameter estimation video

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Area of Science:

Signal Processing
Image Processing
Video Compression

Background:

Compressive Sensing (CS) enables signal acquisition at sub-Nyquist rates.
Traditional CS methods often lack adaptability for varying signal complexities.
Efficient video compression is crucial for storage and transmission.

Purpose of the Study:

To develop an adaptive rate Compressive Sensing (CS) method for video signals.
To improve the efficiency and performance of video CS.
To enable CS implementation on resource-constrained devices.

Main Methods:

Utilizing a Blocked Compressive Sensing (BCS) scheme for video frame blocking and measurement.
Estimating block sparsity using mean and variance from CS measurements for adaptive rate sampling.
Implementing a reference block subtraction method leveraging sparsity estimates for inter-frame correlation.

Main Results:

Accurate estimation of block sparsity and effective block classification based on CS measurements.
Demonstrated adaptive rate sampling by assigning different rates to different block classes.
Achieved superior reconstructed image quality compared to state-of-the-art adaptive CS video methods.

Conclusions:

The proposed adaptive CS method offers accurate sparsity estimation and classification.
The method is computationally simple and suitable for hardware implementation.
It significantly enhances reconstructed video quality while adapting sampling rates efficiently.