Image and Video Processing

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Course Syllabus

Name of the Course: Image and Video Processing
LTP structure of the course: 2-1-1
Objective of the course:
- To provide the basic understanding of the digital image formation and visualization.
- To provide the visualization of relationships between spatial and frequency.
- To provide the understanding of mapping the signal processing techniques to the digital image.
- To provide an idea of multimedia data (image, video).
- To provide an exposure to various image and video compression standards.
Outcome of the course:
- The students shall be able to apply the knowledge gained during the course to solve various real time problems.
- The students shall be able to develop new state of the art image and video processing method.
Course Plan:

-	Unit	Topics for Coverage
Component 1	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.
Component 1	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) & High‐Pass (Sharpening), Ideal, Butterworth and Gaussian Filtering, Unsharp Masking and High‐Boost Filtering, Homomorphic Filtering, Periodic Noise Reduction and Inverse Filtering & Wiener Filtering.
Component 2	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 & Feature Extraction: Model‐based and probabilistic methods and Image Classification Optimal and Multilevel Thresholding, Gray Image Segmentation, Watershed Algorithm.
Component 2	Unit 4	Compression: Lossy and Lossless compression techniques, JPEG, JPEG2000 and Variants, Introduction to video processing, Compression standards and formats (MPEG & H.XXX), Video Streaming.