Name
Abstract | ||
|---|---|---|
The traditional image compression technique uses the redundancy of the image data to compress the image without lossless coding by the corresponding coding techniques. Different compression coding techniques are redundant for different image data. Under the background of many new applications, the traditional image compression technology can no longer meet the requirement of further improving the quality of compressed images. In recent years, with the continuous development of artificial neural network technology, Some good properties of neural network, such as nonlinear, fault-tolerant, self-organizing and adaptive, has been widely applied in image processing technology, which greatly simplifies the complexity of image processing. This paper mainly introduces the basic principles of BP neural network, studies the application of neural network in image compression, carries out simulation experiments through MATLAB, and analyzes the feasibility and advantages of BP neural network applied to image compression. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8527016 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
BP Neural network,Image compression,Generalization ability | Iterative reconstruction,MATLAB,Nonlinear system,Pattern recognition,Computer science,Image processing,Coding (social sciences),Redundancy (engineering),Artificial intelligence,Artificial neural network,Image compression | Conference |
Volume | ISSN | ISBN |
2 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng-Cheng Li | 1 | 0 | 0.34 |
| Gongfa Li | 2 | 239 | 43.45 |
| Ying Sun | 3 | 291 | 40.03 |
| Guozhang Jiang | 4 | 172 | 27.25 |
| Jian-yi Kong | 5 | 11 | 3.65 |
| Shuang Xu | 6 | 274 | 32.53 |