ALGORITHM 1: Blurred degree calculation with ISVD. Input: image I Output: blurred degree b of image I Initialization: b = 0, num = 0 1 convert I into grayscale [I.sub.gray] 2 calculation singular value W = diag([[delta].sub.1], [[delta].sub.2], ..., [[delta].sub.n]) with singular value decomposition on [I.sub.gray] 3 for [[delta].sub.i] in W 4 if [[delta].sub.i] [greater than or equal to] [C.sub.thres] 5 num++ 6 end 7 end 8 return blurred degree b = num/n The image blurred degree calculation with ISVD is summarized as Algorithm 1.
Performance Evaluation of ISVD. This paper applies the video quality assessment index proposed by the Video Quality Experts Group (VQEG) to test the performance of different blurred degree assessment algorithms.
With different blurred degree assessment algorithms (Marziliano, JNB, CPBD, SIGD, and ISVD), we calculate the blurred degree for 174 images in dataset LIVE .
As shown in Table 2, ISVD is less significantly efficient than SIGD, but it is sufficient for real-time VO.
A system such as ISVD can help inquirers convert impersonal facts or data to personal information, and may thereby lead them to comprehension of epigenesis in their individual patterns of career development through analysis, practice, and understanding of decision-making development.
In ISVD, Tiedeman (1970) used slides to show pictures of college campuses and invested much effort in developing natural language capability so that the user could communicate in his or her own language rather than being subjected to multiple-choice menus.