SylabUZ
| Course name | Digitization problems |
| Course ID | 11.3-WE-INFD-DP-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | Computer Science |
| Education profile | academic |
| Level of studies | Second-cycle Erasmus programme |
| Beginning semester | winter term 2022/2023 |
| Semester | 2 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Credit with grade |
| Laboratory | 30 | 2 | - | - | Credit with grade |
| Project | 15 | 1 | - | - | Credit with grade |
To provide basic knowledge about data digitization. To provide understanding of the role of digital data processing techniques in technique and society development. To provide basic skills in modeling of systems for digital data processing, filtering and compression. To provide basic skills in using compression techniques of images and video sequences.
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Acquisition and storage of digital data. Sampling, A/D conversion. Elementary types of digital signals, digital signal ambiguity, filter concept. Analysis in the time domain.
Acquisition of digital data. Converters, digital signal representation.
Modeling of digital systems. Components of the digital data processing system, mathematical modeling of digital data processing systems.
Representation of the signal in the digital system. Decoding, quantizing. Fourier transform. Analysis in the frequency domain. DCT and DWT transformation.
Digital data processing algorithms.
Compression of data: assumptions, classification of methods and algorithms, examples.
Wavelet and hybrid codecs of video sequences.
Subjective and objective measures of quality of image compression.
Lecture, laboratory exercises, project.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – the passing condition is to obtain a positive mark from the final test and oral presentation .
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.
Project - the passing condition is to obtain a positive mark from the final report
Calculation of the final grade: lecture 40% + laboratory 30% + project 30%
Modified by dr inż. Andrzej Popławski (last modification: 19-04-2022 18:58)
