A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment. (January 2018)

• Record Type: Journal Article
• Authors: Shi, Peng; Li, Bin; Thike, Phyu Hnin; Ding, Lianhong
• Is Part Of: International journal of distributed sensor networks. Volume 14:Number 1(2018)
• Publication Date: 2018-01
• Subjects: Coordinate transformation -- edge detection -- high-throughput experiment -- knowledge embedded -- intelligent cyber-physical system -- lossless image compression; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717750374
• Languages: English
• ISSNs: 1550-1329

Abstract

High-throughput experiment refers to carry out a large number of tests and attain various characterizations in one experiment with highly integrated sample or facility, widely adopted in biology, medicine, and materials areas. Consequently, the storing and treating of data bring new challenges because of large amount of real-time data, especially high-resolution images. To improve the storing and treating efficiency of high-throughput image, a knowledge-embedded lossless image compressing method is proposed. Based on the similarity of a series of high-throughput images, it accomplishes the high compression ratio according to the difference between the target images and one reference image. Meanwhile, the knowledge extracted from the image, such as edge information and differences from the reference image, is recorded into the compressed file. The key steps include similarity comparison, edge detection, coordinate transformation, and dictionary encoding. The method has been successfully applied into high-throughput corrosion experiment facility, a typical intelligent cyber-physical system. To evaluate the performance, corrosion metal, face, and flower images are compressed by our method and other lossless image compression methods. The results show that our method has fairly high compression ratio. Moreover, the embedded knowledge can be read directly from the compressed file to support further study.