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Related Experiment Video

Updated: Oct 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information.

Wei Wang¹, Jianming Wang¹, Jianhua Chen¹

¹School of Information Science and Engineering, Yunnan University, Kunming 650000, China.

Entropy (Basel, Switzerland)

|September 28, 2021

Summary

This summary is machine-generated.

This study introduces an adaptive compressed video sensing method using saliency detection and side information to optimize block sparsity estimation. The approach improves reconstructed frame quality and peak signal-to-noise ratio (PSNR) at the same sampling rate.

Keywords:

compressed sensing fusion sparsity saliency detection side information

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Area of Science:

Signal Processing
Computer Vision
Information Theory

Background:

Block-based compressed sensing requires accurate measurement number allocation per block.
Practical constraints limit direct sparsity estimation from original signals.
Existing methods struggle with efficient sparsity estimation without the original signal.

Purpose of the Study:

To propose an adaptive block-based compressed video sensing scheme.
To enhance measurement number allocation using saliency detection and side information.
To improve reconstructed frame quality and reduce block effects.

Main Methods:

Utilizing the Johnson-Lindenstrauss lemma for saliency detection from initial measurements.
Generating a side information frame via a probability fusion model for sparsity estimation.
Fusing saliency values and significant coefficient proportions for robust block sparsity estimation.
Implementing a global recovery model with weighting to mitigate block effects.

Main Results:

The proposed scheme effectively estimates block sparsity by integrating intra-frame and inter-frame correlations.
Adaptive measurement number allocation based on fused sparsity is achieved.
The global recovery model successfully reduces block effects in reconstructed frames.
Experimental results demonstrate significant PSNR improvement compared to existing methods at identical sampling rates.

Conclusions:

The adaptive scheme offers superior performance in compressed video sensing.
Effective fusion of saliency and side information enhances sparsity estimation accuracy.
The method provides a practical solution for measurement allocation in real-world compressed sensing applications.