Digital Image and Video Processing
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Follow on Instagram for UpdatesDigital Image and Video Processing Syllabus — Unit-wise Content
The official GTU curriculum for Digital Image and Video Processing (subject code 3714104) as prescribed for EAC( · Semester 1 divides the course into 6 units, covering 43 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3714104)Unit 1 Digital Image and Video Fundamentals
- Digital image and video fundamentals and formats
- 2-D and 3-D sampling and aliasing
- image decimation/ interpolation
- video sampling and interpolation
- Basic image processing operations
- Image Transforms
- Need for image transforms, DFT, DCT, Walsh
- Hadamard transform
- Haar transform
- Wavelet transform
Unit 2 Image and Video Enhancement and Restoration
- Point processing
- image restoration
- algorithms for 2-D motion estimation
- change detection
- motion-compensated filtering
- frame rate conversion
- video resolution enhancement
- Image and Video restoration (recovery)
Unit 3 Image and Video Segmentation
- Discontinuity based segmentation- Line detection
- edge detection
- Region based segmentation
- Scene Change Detection
- Spatiotemporal Change Detection
- Motion Segmentation
- Simultaneous Motion Estimation and Segmentation Semantic Video Object Segmentation
- Morphological image processing
Unit 4 Colour image Processing
- Colour fundamentals
- Colour models
- Conversion of colour models
- Pseudo colour image processing
- Full colour processing
Unit 5 Image and Video Compression
- Lossless image compression including entropy coding
- lossy image compression
- video compression techniques, and international standards for image and video compression (JPEG
- Video Quality Assessment
Unit 6 Object recognition
- Image Feature representation and description-boundary representation
- boundary descriptors
- regional descriptors
- feature selection techniques
- introduction to classification
- supervised and unsupervised learning
- Template matching
- Bayes classifier
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