Important RGPV Question
EC-803 (B) Digital Image Processing
VIII Sem, EC
UNIT 1-Digital Image Processing (DIP)
Q.1) Why Hadamard transform is most suitable for Digital image processing?
Q.2) Explain Sampling and Quantization. Discuss their various types.
Q.3) Discuss the properties of Fourier Transform.
Q.4) What do you understand by Digital Image Processing? Define its fundamental steps.
Q.5) Give the difference between RGB and HSI Model. Also explain the Adaptive filter. Where it is used?
Q.6) What is chromaticity diagram? Explain how it is useful in colour image processing.
Q.7) Discuss about contrast stretching and intensity slicing.
UNIT 2-Image Transforms
Q.1) What are the advantages of Wiener Filter?
Q.2) What are the properties of Slant Transform?
Q.3) What are the operating modes of JPEG format.
Q.4) Why Hadamard transform is most suitable for Digital image processing?
Q.5) What do you mean by Gaussian Noise and why averaging filter is used to eliminate it?
Q.6) Explain Haar transform mean? Elaborate the process for calculating the Haar transformation matrix.
Q.7) What do you understand by Image Transform? Discuss various types of Transforms.
Q.8) Define the dimensional sampling theorem for band images. How limited the constructed from its samples? Explain.
UNIT 3-Image Enhancement Spatial Domain Methods
Q.1) Define image segmentation.
Q.2) What is meant by pixel depth?
Q.3) Explain Homomorphic Filtering.
Q.4) Explain about color image smoothing.
Q.5) Discuss the process of image smoothing using ideal low pass filters and Butterworth low passfilters.
Q.6) What benefits can adaptive filters offer? Describe the adaptive median filter.
Q.7) Explain image compression system with the help of functional block diagram.
Q.8) Describe the steps involved in changing colors from HSI to RGB.
Q.9) Explain how periodic noise can be removed from image using frequency domain filtering.
Q.10) What do you mean by Image Segmentation? What are the various segmentation techniques?
Q.11) Explain the morphological operations: Opening and Closing.
Q.12) What do you mean by Image Restoration? Discuss Image degradation model and also explain different noise modeling.
Q.13) Explain how the two types of edge detection methods based on first derivatives and second derivatives work for this type of edge. Describe how these methods work in terms of the edge detector optimality criteria—edge detection, edge localization and one-response to an edge.
UNIT 4-Image Restoration
Q.1) Define image segmentation.
Q.2) What is the need of picture compression?
Q.3) Describe the application area of image processing in detail.
Q.4) Discuss with the help of mathematical equations.
Q.5) Discuss and Differentiate between the image restoration and enhancement.
Q.6) Explain Edge Linking using Hough Transform.
Q.7) Compare the Inverse with Wiener filter. Discuss the Wiener smoothing filter.
UNIT 5-Image Compression Fundamentals Of Data Compression
Q.1) Explain Hit-or-Miss transformation.
Q.2) Identify the problems in region based segmentation.
Q.3) What is meant by Image Compression? Clear your answer why it is very essential step in image processing with the suitable examples.
Q.4) Give detail Huff coding, Run length coding and Transform coding.
Q.5) Consider an 8-pixel line of gray-scale data, (12, 12, 13, 10, 13, 57, 54), which has been uniformly quantized with 6-bit accuracy. Construct its 3-bit IGS code.
Q.6) A 3-bit image has pixel distribution as following and takes 3-bits for fixed length compression. Apply and show the working of Huffman coding for compressing the same image and find variable length coding.
— Best of Luck for Exam —
