پديدآورندگان :
Irannejad Maziar Maziyar_irannezhad@yahoo.com Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran , Mahdavi Nasab Homayoun mahdavinasab@iaun.ac.ir Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran
چكيده فارسي :
In this paper, we propose an adaptive Digital image sequence compression stored by fixed cameras via dictionary learning. This method transforms images over sparsely tailored, over-complete dictionaries learned directly from image samples rather than a fixed one, and thus can approximate an image with fewer coefficients. In this research for compression of each frame of the image sequence by our proposed method, we used two different dictionary learning algorithms (RLS-DLA and K-SVD) to compare the operation each of them. Dictionaries are learned in DCT domain and wavelet domain. The results show that the RLS-DLA has better performance than K-SVD. Also the performances of wavelet domain have better results.
