Abstract—In this paper, we propose Transform-Domain algorithms to effectively classify the characteristics of blocks and estimate the strength of the blocky effect. The Transform-Domain algorithms require much lower computational complexity and much less memory than the spatial ones. Along with the estimated blocky strength,
標簽： Transform-Domain effectively algorithms Abstract
上傳時間： 2017-06-16
上傳用戶：zukfu
System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares
標簽： Transform-Domain identification partial-update Least-Mean
上傳時間： 2014-01-12
上傳用戶：ztj182002
Compress an decomprres DCT=Domain Transform Coding for image.
標簽： decomprres Transform Compress Coding
上傳時間： 2013-12-17
上傳用戶：kr770906
This program produces a Frequency Domain display from the Time Domain * data input using the Fast Fourier Transform.
標簽： Domain Frequency the produces
上傳時間： 2013-12-11
上傳用戶：mpquest
Fast Fourier Transform power point The rectangular window introduces broadening of any frequency components [`smearing鈥? and sidelobesthat may overlap with other frequency components [`leakage鈥?. 鈥he effect improves as Nincreases 鈥owever, the rectangle window has poor properties and better choices of wncan lead to better spectral properties [less leakage, in particular] 鈥搃.e. instead of just truncating the summation, we can pre-multiply by a suitable window function wnthat has better frequency domain properties. 鈥ore on window design in the filter design section of the course
標簽： rectangular introduces broadening Transform
上傳時間： 2017-03-25
上傳用戶：change0329
The frequency domain plays an important role in image processing to smooth, enhance, and detect edges of images. Although image data typically does not include imaginary values, the fast Fourier transform (FFT) has been used for obtaining spectra. In this paper, the fast Hartley transform (FHT) is used to transform two-dimensional image data. Because the Hartley transform is real valued, it does not require complex operations. Both spectra and autocorrelations of two-dimensional ultrasound images of normal and abnormal livers were computed.
標簽： processing frequency important enhance
上傳時間： 2014-01-08
上傳用戶：1051290259
An important book that cover wavelet domain. It help a lot in understanding wavelets and their applications. Beside explanations of the transform and application of this, there are also presents the reasons why wavelets are better than Fourier transform.
標簽： understanding important wavelets wavelet
上傳時間： 2017-06-14
上傳用戶：lanhuaying
Linux domain sockets 編程
上傳時間： 2013-12-18
上傳用戶：凌云御清風
個人資源整理器,具體請訪問我的網站 user:alfried passwd:alfried domain:workgroup
標簽： alfried workgroup passwd domain
上傳時間： 2015-02-23
上傳用戶：gaome
h.264最新transform算法介紹
上傳時間： 2014-08-15
上傳用戶：tonyshao
