Multimedia Information
A.Y. 2019/2020
Learning objectives
The course aims at studying how multimedia information is acquired, coded and processed by the most popular applications running on common multimedia systems. For this reason, the course will be focused on theoretical and practical elements particularly important for the manipulation of the three main multimedia modalities, i.e. images, videos and audios, which are the core of modern communication.
Expected learning outcomes
Students will learn the basic notions and tools about how information conveyed by images, videos and audios is handled and processed, which in turns means to be skilled at:
- capturing multimedia information through digital systems such as video cameras, TV or computers
- coding and representing multimedia data using fundamentals and techniques of information theory
- using algorithms and programming languages devoted to multimedia data processing
- capturing multimedia information through digital systems such as video cameras, TV or computers
- coding and representing multimedia data using fundamentals and techniques of information theory
- using algorithms and programming languages devoted to multimedia data processing
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
IMAGES:
- Introduction to image acquisition, digitalization and processing
- light, color and the electromagnetic spectrum
- punctual transformations for image enanchement
- image filtering in spatial and frequency domain
- clusteting and image binarization
- introduction to the classification via convolutional neural networks
- key point detection and local descriptors
- examples and simulations using Matlab
VIDEO:
- image quality augmenting filters
- video segmentation
- examples and simulations using Matlab
- analog and digital video formats
- compression and video coding in the standard MPEG* and H.26*
- examples and simulations using Matlab
AUDIO:
- Sound digitalization
- filters for sound analysis
- digital audio synthesis and effects
- MPEG* audio coding standard
- examples and simulations using Matlab
- Introduction to image acquisition, digitalization and processing
- light, color and the electromagnetic spectrum
- punctual transformations for image enanchement
- image filtering in spatial and frequency domain
- clusteting and image binarization
- introduction to the classification via convolutional neural networks
- key point detection and local descriptors
- examples and simulations using Matlab
VIDEO:
- image quality augmenting filters
- video segmentation
- examples and simulations using Matlab
- analog and digital video formats
- compression and video coding in the standard MPEG* and H.26*
- examples and simulations using Matlab
AUDIO:
- Sound digitalization
- filters for sound analysis
- digital audio synthesis and effects
- MPEG* audio coding standard
- examples and simulations using Matlab
Prerequisites for admission
Fundamentals of digital signal processing
Teaching methods
The course consists of lectures and practical classes based on Matlab programming.
Teaching Resources
The lecture slides, the suggested books and Matlab exercises are available on the Ariel portal http://ggrossiim.ariel.ctu.unimi.it/v5/home/Default.aspx and on the course page http://im.di.unimi.it.
Assessment methods and Criteria
The examination consists of two parts:
1. a written test based on the lecture subjects (75% of final grade)
2. a practical test based on Matlab programming (25% of final grade)
Two partial exams are provided at mid- and end-course respectively.
1. a written test based on the lecture subjects (75% of final grade)
2. a practical test based on Matlab programming (25% of final grade)
Two partial exams are provided at mid- and end-course respectively.
Elements of Image Processing
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Lanzarotti Raffaella
Shifts:
-
Professor:
Lanzarotti Raffaella
Elements of Video Processing
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Grossi Giuliano
Shifts:
-
Professor:
Grossi GiulianoProfessor(s)