Multimedia Information
A.Y. 2024/2025
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 can be attended as a single course.
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 enhancement
- Image filtering in spatial domain
- Image segmentation and clusteting
- Key point detection and local descriptors
- Introduction to the classification via convolutional neural networks
- Examples and simulations with Python
VIDEO:
- Optical flow and tracking
- Video segmentation (via background subtraction)
- Analog and digital video formats
- Classification and recognition tasks
- Compression and video coding in the standard JPEC, MPEG* and H.26*
- Examples and simulations with Python
AUDIO:
- The physics of sound
- Digitization of sound
- Audio spectral analysis
- Digital filters for audio
- Parametric equalizers
- Delay-based audio effects
- Mel spectrogram and MFCC
- Classification of audio with DNN
- Examples and simulations with Python
- Introduction to image acquisition, digitalization and processing
- Light, color and the electromagnetic spectrum
- Punctual transformations for image enhancement
- Image filtering in spatial domain
- Image segmentation and clusteting
- Key point detection and local descriptors
- Introduction to the classification via convolutional neural networks
- Examples and simulations with Python
VIDEO:
- Optical flow and tracking
- Video segmentation (via background subtraction)
- Analog and digital video formats
- Classification and recognition tasks
- Compression and video coding in the standard JPEC, MPEG* and H.26*
- Examples and simulations with Python
AUDIO:
- The physics of sound
- Digitization of sound
- Audio spectral analysis
- Digital filters for audio
- Parametric equalizers
- Delay-based audio effects
- Mel spectrogram and MFCC
- Classification of audio with DNN
- Examples and simulations with Python
Prerequisites for admission
Fundamentals of digital signal processing
Teaching methods
The course consists of lectures and practical classes based on Python.
Teaching Resources
The lecture slides, the suggested books, and Python exercises are available on the Ariel portal http://ggrossiim.ariel.ctu.unimi.it/v5/home/Default.aspx and 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.
Elementi di Elaborazione Audio e Video
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professors:
Grossi Giuliano, Lanzarotti Raffaella
Shifts:
Elementi di Elaborazione Immagini
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Lanzarotti Raffaella
Shifts:
Turno
Professor:
Lanzarotti RaffaellaEducational website(s)
Professor(s)