A Traffic Image Compression Technique of Selfadapt Parameter Choice
Wenlun Cao, Shi Zhong Ke, Feng Hu · 2006
There is data that shows that economic lose of several ten millions to a few hundred millions US$ at many prosperous nations every year because of traffic jam. One aspect of ITS (intelligence transportation system) aims at the characteristics of the traffic image. We plan to combine image compression and traffic application together. The basic thought of our method is make the image data into one dimensional data row using some kind of image scanning method. The scan data is unsteady usually. We must monotonize it automatically through machine learning before the polynomial approach. We use the polynomial approach to these data row, the record coefficient attain the purpose of the compression image