Research on real-time data transmission and multi-scale video image decomposition of embedded optical sensor array based
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Research on real-time data transmission and multi-scale video image decomposition of embedded optical sensor array based on machine learning Mingxin Cai 1
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& Shanshan Wang & Chao Wu
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Received: 5 May 2020 / Revised: 29 July 2020 / Accepted: 9 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Aiming at the research of real-time data transmission and multi-scale image decomposition of embedded optical sensor array, the principle, method and fusion strategy of multisensor image fusion are studied comprehensively, thoroughly and systematically by combining the imaging characteristics of source image with multi-scale geometric analysis tools using machine learning algorithm. A new quality scalable video image coding framework is also proposed in this paper, which is implemented by a multi-scale online dictionary learning algorithm based on structured sparse video signals. For the purpose of different types of images and image fusion, a new high quality scalable video image coding framework based on machine learning algorithm is proposed on the basis of comprehensive analysis of prior information such as imaging mechanism of image sensor and imaging characteristics of source image. A multi-scale online dictionary learning algorithm based on machine learning for sparse video signal structure is proposed. Through the hierarchical structure of wavelet decomposition, the searching domain of online learning is optimized to a hierarchical sparse block, and its sparse representation coefficients are obtained by using machine learning sparse coding idea. The real-time data transmission of embedded optical sensor array based on machine learning and multi-scale image decomposition algorithm proposed in this paper have good fusion performance, which is of great significance for further research and engineering application of image fusion technology. Keywords Embedded . Sensor . Data transmission . Spatiotemporal online dictionary learning . Multi-scale image . Machine learning
* Mingxin Cai [email protected]
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Collage of Computer Science, Northeastern University, Shenyang 110169, China
Multimedia Tools and Applications
1 Introduction Machine learning means that the system improves its performance according to experience. Machine learning is one of the most active and potential fields in artificial intelligence. In the past 30 years, machine learning research has made unprecedented progress. On September 14, 2001, Dennis DeCoste, a scientist at NASA’s Jet Propulsion Laboratory in California, pointed out that machine learning is playing an increasingly supportive role in every stage of scientific research, and that this field will achieve steady and rapid development and make greater contributions to science in the coming years. From their evaluation of machine learning, we can see the research value of machine learning. Many embedded vision applications use only one image sensor to monitor one direction, such as the front of the car. The image sensor can detect, classify a
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