چکیده مقاله
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
کلیدواژهها
نویسندگان
Maziar Irannejad
Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, IranIranian Oil Pipeline and Telecommunication company
Homayoun Mahdavi Nasab
Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran
شیوه ارجاع
Irannejad, Maziar and Mahdavi Nasab, Homayoun,1395,Adaptive Digital Image Sequence Compression Stored by Fixed Cameras Base on Sparse Representation and Dictionary Learning, International Conference on Engineering and Computer Science,Najafabad
ارائهشده در
مجموعه مقالات کنفرانس بین المللی مهندسی و علوم کامپیوتر3 اسفند 1395 · نجف آباد