Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Image and Video Processing

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CVBL Home CVBL Courses

Offerings

• Image and Video Processing, July-Dec 2023
• Image and Video Processing, July-Dec 2022

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 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.