文件名称:hw2
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 1kb
- 下载次数：
- 0次
- 提 供 者：
- zhen****
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1.对一个256*256的图像进行DCT变换得到图像D，将D得斜下角数值置为零，然后进行DCT反变换.
2.对源图像进行K-L转换
1和2比较-1.Get a grey level image which size is N*N. (For example, 256*256, however,
N = ), and partition to 8*8 sub images.
2.. Apply DCT to these sub images, and get the transformed image D with DCT
coefficients for elements.
3. From D, keep the coefficient values for only upper left triangular region and set
zeros for lower right region to approximate the image. (That is, only half of data
is used.)
4.Take Inverse DCT to get the approximated image.
2 . Get the covariance matrix of image.
3 . Calculate the corresponding eigenvectors and eigenvalues.
4 . Represent the original image with Singular Value Decomposition.
5 . Approximate the image by taking off the 4 smallest eigenvalues. (That is, only
half of information is used.)
2.对源图像进行K-L转换
1和2比较-1.Get a grey level image which size is N*N. (For example, 256*256, however,
N = ), and partition to 8*8 sub images.
2.. Apply DCT to these sub images, and get the transformed image D with DCT
coefficients for elements.
3. From D, keep the coefficient values for only upper left triangular region and set
zeros for lower right region to approximate the image. (That is, only half of data
is used.)
4.Take Inverse DCT to get the approximated image.
2 . Get the covariance matrix of image.
3 . Calculate the corresponding eigenvectors and eigenvalues.
4 . Represent the original image with Singular Value Decomposition.
5 . Approximate the image by taking off the 4 smallest eigenvalues. (That is, only
half of information is used.)
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