Paper Info
Title
Measurement Structures of Image Compressive Sensing for Green Internet of Things (IoT).
Abstract
Image compressive sensing (CS) is a potential imaging scheme for green internet of things (IoT). To further make CS-based sensor adaptable to low bandwidth and low power, this paper focuses on finding a good measurement structure, i.e., the organization and storage format of CS measurements. Three potential measurement structures are proposed in this paper, respectively raster structure (RA), patch structure, and layer structure (LA). RA stores CS measurements of each column in an image, and PA packets CS measurements of overlapping patches forming an image. LA enables the measuring of small blocks and recovery of large blocks. All of the three structures avoid high computation complexity and huge memory in the process of measuring and recovery, and efficiently suppress the annoying blocking artifacts which often occur in traditional block structures. Experimental results show that RA, PA, and LA can efficiently reduce blocking artifacts, and produce comforting visual qualities. LA, especially, presents both good time-distortion and rate-distortion performance. By this paper, it is proved that LA is a suitable measurement structure for green IoT.
Year
DOI
Venue
2019
10.3390/s19010102
SENSORS
Keywords
Field
DocType
image compressive sensing (CS),green internet of things (IoT),measurement structure,random structural matrices,linear recovery
Raster graphics,Internet of Things,Network packet,Electronic engineering,Bandwidth (signal processing),Engineering,Computer hardware,Compressed sensing,Computation complexity
Journal
Volume
Issue
ISSN
19
1
1424-8220
Citations 
PageRank 
References 
0
0.34
18
Authors
3
Name
Order
Citations
PageRank
Ran Li1306.80
Xiaomeng Duan211.71
Yan-ling Li3214.28