Indian Institute of Information Technology, Allahabad

Image and Video Processing (IVP)

Aug-Dec 2022 Semester (MTech)


Course Information

Course Description: This course provides the basic understanding of the digital image formation and visualization, the visualization of relationships between spatial and frequency, the understanding of mapping the signal processing techniques to the digital image, an idea of multimedia data (image, video), and an exposure to various image and video compression standards.

Course Outline (Topics): The following list of topics is tentative. Based on available time slots, some topics may be dropped or added or reordered.

Unit 1: Digital Image Fundamentals- Simple image model, digital image formation, sampling, quantization, resolutions and representation, relationship among pixels, types of digital images. Color Image Processing: Color Representation, Chromaticity Diagram and Color Spaces, types of digital imaging and application areas. Enhancement- Point Processing: Contrast Stretching, Power-law and Gamma Transformation. Histogram Processing: Histogram Equalization and Matching.

Unit 2: Filtering and Restoration- Degradation function and Noise Models, Spatial Domain Filtering: Correlation and Convolution, Smoothing Linear and Nonlinear Filters: Mean and Median Filters, Adaptive Filtering, Sharpening Linear and Nonlinear Filters: Derivative, Laplacian, Unsharp Masking, High-boost Filtering. Frequency Domain Filtering: Filtering: Low-pass (Smoothing) and High-Pass (Sharpening), Ideal, Butterworth and Gaussian Filtering, Unsharp Masking and High-Boost Filtering, Homomorphic Filtering, Periodic Noise Reduction and Inverse Filtering and Wiener Filtering.

Unit 3: Edges, Lines and Boundary Detection- First and Second Order Edge Operators, Multi-scale Edge Detection, Canny Edge Detection Algorithm, Hough Transform: Line and Edge Detection, Morphological Operations and Application: Boundary, Skelton, Convex-Hull, Thinning, Pruning etc. Segmentation and Feature Extraction: Model-based and probabilistic methods and Image Classification Optimal and Multilevel Thresholding, Gray Image Segmentation, Watershed Algorithm.

Unit 4: Compression: Lossy and Lossless compression techniques, JPEG, JPEG2000 and Variants, Introduction to video processing, Compression standards and formats (MPEG and H.XXX), Video Streaming.

Course Instructor

Dr. Shiv Ram Dubey

TAs

  • Shivani Mohan Agarwal - MIT2021036
  • Akshay Jain - MIT2021038
  • Ayush Dubey - MIT2021040
  • Trinetra Devkatte - MIT2021096

Class Schedule
Class: Wednesday 10.00 AM - 12.00 Noon, Tute: Thursday 09.00 AM - 11.00 AM, Lab: Tuesday 03.00 PM - 05.00 PM
Course Ethics
  • Students are strictly advised to avoid the unethical practices in the course including review tests and practice components.
  • It is best to try to solve problems on your own, since problem solving is an important component of the course.
  • You are allowed to discuss class material, problems, and general solution strategies with your classmates. But, when it comes to formulating or writing solutions you must work/implement by yourself.
  • You are not allowed to take the codes from any source, including online, books, your classmate, etc. in the assignments and exams.
  • You may use free and publicly available sources (at idea level only), such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose marks for using external sources only at idea level.)
  • You may not use any paid service and you must clearly and explicitly cite all outside sources and materials that you made use of.
  • Students are not allowed to post the code/report/any other material of course project in public domain or share with any one else without written permission from course instructors.
  • We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the Institute's policies on academic honesty.
  • Instances will be dealt with harshly and typically result in a failing course grade.
  • Cheating cases will attract severe penalties.

Schedule

Date Topic Class Material
L01-08: Aug 24-25, 2022, Aug 31, 2022, Sept 01, 2022 Introduction and Fundamentals Slide
L09-10: Sept 07, 2022 Intensity Transformation Slide
L11-12: Sept 08, 2022 Filtering in Spatial Domain Slide
L13-16: Sept 14-15, 2022 Filtering in Frequency Domain Slide
L17-18: Sept 21, 2022 Image Restoration Slide
L19-22: Oct 12-13, 2022 Morphological Image Processing Slide
L23-25: Oct 19, Nov 02, 2022 Image Segmentation Slide
L26: Nov 02, 2022 Color Image Processing Slide
L27-30: Nov 09-10, 2022 Image Compression Slide
L31: Nov 16, 2022 Image Compression Standards Slide
L32: Nov 16, 2022 Video Processing Slide

Grading

  • C1 (30%)
  • C2 (30%)
  • C3 (40%)

Prerequisites

  • Computer Programming
  • Data Structures
  • Problem Solving
  • Ability to deal with abstract mathematical concepts

Books/References

  • Digital Image Processing by Willam K. Pratt, John Willey & Sons
  • Digital Image Processing by Gonzalez, Rafael C., and Richard E. Woods, Pearson Education
  • The Essential Guide to Video Processing by Alan C. Bovik, Academic Press

Disclaimer

The content (text, image, and graphics) used in the slides are adopted from many sources for Academic purposes. Broadly, the sources have been given due credit appropriately. However, there is a chance of missing out some original primary sources. The authors of this material do not claim any copyright of such material.