Jawaharlal Nehru Technological University Hyderabad B.Tech IV DIGITAL IMAGE PROCESSING Previous Question Paper (February/March - 2022 - R18)

Digital image processing involves a sequence of fundamental steps to transform an input image into a desired output or information. These steps include:

1. Image Acquisition: Capturing the image via sensors (e.g., CCD cameras). Example: Capturing a photograph of a document.
2. Image Enhancement: Improving the visual appearance or highlighting features. Example: Adjusting contrast or brightness to make a dark image clearer.
3. Image Restoration: Improving the appearance of an image based on a mathematical model of degradation. Example: Removing blur caused by camera motion.
4. Color Image Processing: Manipulating color models (RGB, CMYK) to enhance or analyze images. Example: Color balancing in digital photography.
5. Wavelets and Multi-resolution Processing: Representing images at various degrees of resolution. Example: Image compression (JPEG 2000).
6. Compression: Reducing data size for storage or transmission. Example: Converting a BMP file to a smaller JPEG file.
7. Morphological Processing: Extracting image components useful for representation and description. Example: Thinning or thickening characters in OCR.
8. Segmentation: Partitioning an image into its constituent parts or objects. Example: Isolating a tumor from an X-ray scan.
9. Representation and Description: Converting data into a form suitable for computer processing. Example: Extracting boundary features of an object.
10. Object Recognition: Assigning a label to an object based on its descriptors. Example: Identifying a face in a security feed.

Quantization is the process of mapping continuous intensity values (amplitudes) of a sampled image into a finite set of discrete levels. While sampling discretizes the spatial coordinates, quantization discretizes the amplitude values.

It is needed because digital computers have finite memory and storage capacity; they cannot represent an infinite range of continuous intensity values. By mapping these values to a fixed number of bits (e.g., 8-bit depth providing 256 levels), we make the image data manageable for digital processing and storage.

The primary effects of quantization include:

• False Contouring: If the number of quantization levels is too low, smooth gradients appear as distinct bands of color or gray levels, known as contouring.
• Loss of Detail: Excessive quantization reduces the dynamic range and fine textural details of the image, leading to a loss of information.
• Memory Efficiency: Higher quantization levels (more bits) result in higher image quality but require significantly more storage space and bandwidth.