SECTION A
(Short answers – explained clearly)
1. What is an Image?
An image is a two-dimensional function that represents the visual information of a scene. In digital image processing, an image is defined as a function of two spatial variables that gives the intensity or gray value at each point. A digital image is formed by dividing the image into small elements called pixels, where each pixel stores intensity information.
2. What is segmentation?
Segmentation is the process of dividing an image into meaningful regions or objects based on certain characteristics such as intensity, color, or texture. The main purpose of segmentation is to simplify image representation so that important objects can be easily identified and analyzed. It is widely used in applications like medical imaging and object detection.
SECTION B
(Medium answers – conceptual explanation)
3. What are the fundamental steps in Digital Image Processing? Explain.
The fundamental steps in digital image processing begin with image acquisition, where the image is captured using sensors. This is followed by preprocessing, which includes noise removal and enhancement. Next, image segmentation divides the image into regions of interest. Feature extraction then identifies important attributes such as edges or shapes. Finally, image interpretation and recognition assign meaning to the image content. These steps together enable effective analysis and understanding of images.
4. Explain the Image Compression model.
The image compression model reduces the amount of data required to represent an image without significantly affecting visual quality. It consists of an encoder that removes redundant information using transformation and quantization, followed by entropy coding. At the receiver side, a decoder reconstructs the image from compressed data. Compression helps save storage space and reduces transmission bandwidth.
SECTION C
(Long answers – detailed explanation)
5. Explain sampling and quantization. Also differentiate between uniform and non-uniform sampling and quantization.
Sampling is the process of converting a continuous image into a discrete form by selecting finite points in space. Quantization assigns discrete intensity levels to sampled values. In uniform sampling and quantization, the intervals are equal throughout the image, making implementation simple. In non-uniform sampling and quantization, intervals vary based on image characteristics, providing better quality for important regions but increasing complexity.
6. What do you mean by Gaussian noise and why is an averaging filter used to eliminate it?
Gaussian noise is a type of noise that follows a normal distribution and affects image intensity values randomly. It commonly occurs due to electronic sensor noise during image acquisition. An averaging filter is used to reduce Gaussian noise because it smooths intensity variations by replacing each pixel value with the average of its neighboring pixels. This reduces noise but may slightly blur the image.
