A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment
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 becau...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717750374 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850167546376880128 |
|---|---|
| author | Peng Shi Bin Li Phyu Hnin Thike Lianhong Ding |
| author_facet | Peng Shi Bin Li Phyu Hnin Thike Lianhong Ding |
| author_sort | Peng Shi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6e6d66bdaef48488ebd75d75dfd14d8 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-e6e6d66bdaef48488ebd75d75dfd14d82025-08-20T02:21:10ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-01-011410.1177/1550147717750374A knowledge-embedded lossless image compressing method for high-throughput corrosion experimentPeng Shi0Bin Li1Phyu Hnin Thike2Lianhong Ding3National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing, ChinaNational Center for Materials Service Safety, University of Science and Technology Beijing, Beijing, ChinaNational Center for Materials Service Safety, University of Science and Technology Beijing, Beijing, ChinaSchool of Information, Beijing Wuzi University, Beijing, ChinaHigh-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.https://doi.org/10.1177/1550147717750374 |
| spellingShingle | Peng Shi Bin Li Phyu Hnin Thike Lianhong Ding A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment International Journal of Distributed Sensor Networks |
| title | A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment |
| title_full | A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment |
| title_fullStr | A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment |
| title_full_unstemmed | A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment |
| title_short | A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment |
| title_sort | knowledge embedded lossless image compressing method for high throughput corrosion experiment |
| url | https://doi.org/10.1177/1550147717750374 |
| work_keys_str_mv | AT pengshi aknowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT binli aknowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT phyuhninthike aknowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT lianhongding aknowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT pengshi knowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT binli knowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT phyuhninthike knowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment AT lianhongding knowledgeembeddedlosslessimagecompressingmethodforhighthroughputcorrosionexperiment |