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Related Experiment Video

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An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT).

Ran Li¹, Xiaomeng Duan², Xu Li³

¹School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China. liran@xynu.edu.cn.

Sensors (Basel, Switzerland)

|April 21, 2018

Summary

This study introduces an energy-efficient image coding scheme for Green Internet of Things (IoT) using Compressive Sensing (CS). The method offers low complexity and good reconstruction quality, outperforming JPEG.

Keywords:

Green IoT compressive sensing gradient field image coding linear projection

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Area of Science:

Computer Science
Electrical Engineering
Information Theory

Background:

The proliferation of the Internet of Things (IoT) necessitates energy-efficient data processing.
Traditional image coding methods can be computationally intensive, limiting their application in low-power IoT devices.

Purpose of the Study:

To develop an energy-efficient compressive image coding scheme for Green IoT applications.
To reduce the computational complexity of both image encoding and decoding.

Main Methods:

Utilizing Compressive Sensing (CS) theory for image acquisition and reconstruction.
Implementing an adaptive compressive encoder based on block-based gradient fields.
Developing a real-time decoder using a Minimum Mean Square Error (MMSE) criterion for projection matrix learning.

Main Results:

The proposed scheme demonstrates lower encoding and decoding complexity compared to traditional methods.
Achieved good objective and subjective image reconstruction quality.
Exhibited superior time-distortion performance over JPEG compression.

Conclusions:

The developed compressive image coding scheme is a viable energy-efficient solution for Green IoT.
The low computational complexity contributes to reduced energy consumption.
The scheme offers a competitive alternative to existing image compression techniques for resource-constrained environments.