Course Descrption:
This course introduces fundamentals of image and video processing, including color image capture and representation; image/video compression; video networking; selected advanced image/video processing and communication techniques. Students will learn to implement selected algorithms in Python (Matlab). A term project will be required. Relevant publications will be provided as reference. Through this course, students would have to gain the classical knowledge and cutting-edge techniques.
Prerequisites:
Probability and Random Process, Signal and Systems, Digital Image Processing
Instructor:
Professor Zhan Ma, Room 332B, Email: mazhan@nju.edu.cn . Homepage
Professor Xu Zhang, Room 332B, Email: xuzhang17@nju.edu.cn . Homepage
Teaching Assistants:
Peiyao Guo, Room 311, Email: peiyao1001@sina.com
Course Schedule:
Wedensday 9-10, Room 仙 II - 311.
Office Hour:
Every Wednesday Before and After Class (or By appointment)
Text Book/References:
Y. Wang, J. Ostermann, and Y.Q.Zhang, Video Processing and Communications. Prentice Hall, 2002. 'Link' (Reference for image and video coding, motion estimation, and stereo)
Richard Szeliski, Computer Vision: Algorithms and Applications. (Available online:'Link') (Cover most of the material, except sparsity-based image processing and image and video coding
Grade Policy:
Final: 60%, Mid-term: 20%, Assignments: 20% (Programming & Written)
Syllabus and Course Material:
Week #1 Introduction to the video communication [pdf]
Week #2 Image/Video Capture and Processing [pdf]
Week #3 Image Compression: Transform, Entropy Coding [pdf]
Week #4 Video Compression: Intra and Inter Prediction [pdf]
Week #5 Video Compression: Standards [p1, p2 ] (Courtesy of Prof. Yao Wang at NYU)
Week #6 Modern Media Transport: RTP, HTTP/WebSocket, WebRTC [pdf]
Week #7 Network Measurement [pdf]
Week #8 CDN Caching and Cache De-Duplication [pdf]
Week #9 mid-term (open book, open material)
Week #10 Subjective quality assessment and quality Metrics [pdf]
Week #11 MPEG DASH/MMT standards
Week #12 Learning for Compression
Week #13 Immersive quality models
Week #14 Green Video
Week #15 Collaborative Video Processing
Week #16 Learning for Networking [pdf]
Week #17 Final Project Review
Week #18 Final Project Review