JNTU B.Tech IV IMAGE PROCESSING AND PATTERN RECOGNITION Previous Question Paper (May/June - 2019 - R15)

Quantization is the process of mapping a continuous range of input values (such as the intensity levels of an image) into a finite set of discrete values. In digital image processing, this involves converting the continuous brightness values of an analog image into a fixed number of digital levels, typically represented by a specific number of bits per pixel, which determines the image's dynamic range and storage requirements.

Interpolation is the process of using known data values to estimate unknown values at locations between known pixels in a digital image. It is fundamental in image processing for tasks such as zooming, shrinking, rotating, and geometric correction.

The process involves mapping the coordinates of the output image back to the input image. Since these mapped coordinates are often non-integers, interpolation methods are used to determine the intensity value at these fractional positions based on the surrounding known pixels.

Common interpolation techniques include:

• Nearest Neighbor Interpolation: Assigns the value of the closest pixel in the original image to the new location. It is computationally efficient but can cause jagged edges or aliasing.
• Bilinear Interpolation: Uses the weighted average of the four nearest neighbors. It provides smoother results than nearest neighbor but requires more computation.
• Bicubic Interpolation: Considers the 16 nearest neighbors to calculate the intensity value. It produces the highest quality results with fewer artifacts, though it is the most computationally intensive.