Time: 3 hours Max Marks: 80
Answer any FIVE Questions.
All Questions carries equal marks.
1.a] Define spatial and gray level resolution. Explain about Isopreference curves.
b] Explain about the basic relationships and distance measures between pixels in a digital image. [6+10]
b] Explain about the basic relationships and distance measures between pixels in a digital image. [6+10]
2.a] Explain the properties of the Discrete cosine transform.
b] Explain the properties of Slant transform. [8+8]
b] Explain the properties of Slant transform. [8+8]
3.a] Discuss about the mechanics of filtering in
spatial domain. Mention the points to be considered in implementing
neighborhood operations for spatial filtering.
b] Compare smoothing linear filters and order-static filters. [8+8]
b] Compare smoothing linear filters and order-static filters. [8+8]
4.a] Explain basic steps for filtering in
frequency domain. How do you relate frequency components of Fourier
transform with the spatial variation in the gray levels of the image.
b] Explain how Laplacian is implemented in frequency domain.
c] What is High frequency filtering. [8+4+4]
b] Explain how Laplacian is implemented in frequency domain.
c] What is High frequency filtering. [8+4+4]
5.a] Explain about color image smoothing and sharpening process.
b] Explain about color segmentation process. [8+8]
b] Explain about color segmentation process. [8+8]
6.a] Explain about the restoration filters used when the image degradation is due to noise only.
b] Explain about Wiener filter used for image restoration. [8+8]
b] Explain about Wiener filter used for image restoration. [8+8]
7.a] What are the basic types of gray level discontinuities in a digital image. And how they are detected.
b] Explain the significance of Thresholding in image segmentation. [8+8]
b] Explain the significance of Thresholding in image segmentation. [8+8]
8.a] Define image compression. Explain about data redundancy.
b] Explain with example how Huffman encoding process reduces coding redundancy.
c] Explain about lossy predictive coding.
b] Explain with example how Huffman encoding process reduces coding redundancy.
c] Explain about lossy predictive coding.
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