SylabUZ
| Course name | Digital signal processing and compression |
| Course ID | 11.3-WE-INFD-DSPaC-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 | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
To present the basics of discrete linear systems, spectral analysis, and filtration of discrete signals. Developing the skill of designing SOI and NOI filters. To learn about the basic methods of lossless compression and lossy compression, their properties and applications.
Mathematical analysis
Mathematical representation of continuous and discrete signals. Causal, time-invariant linear systems. Sampling and amplitude quantization of signals, Nyquist-Shannon theorem. Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT). Frequency analysis of signals using DFT. Z transformation, discrete transmittance.
Digital filters, finite impulse response (SOI) filters and infinite impulse response (NOI) filters. Design methods for SOI and NOI filters. Effects of finite register length in digital signal processing.
Lossless compression. Mathematical basis of lossless compression. Huffman coding, arithmetic coding, dictionary coding methods, predictive coding. Applications of lossless compression in text, sound and image compression tasks.
Lossy compression. Mathematical foundations of lossy compression. Scalar quantization, vector quantization, differential coding. Transformational coding, Karhunen-Loev transformation, discrete cosine transform, discrete Walsh-Hadamard transformation. Subband coding, wavelet compression. Applications of lossy compression in audio and image compression tasks.
Lecture: traditional lecture
Laboratorium: lab exercises
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture - the condition of getting credit is obtaining a positive grade from an exam carried out in writing or oral
Laboratory - the condition of getting credit is obtaining positive grades from all laboratory exercises, planned to be implemented under the laboratory program.
1. Lyons R.G.: Understanding Digital Signal Processing, Prentice-Hall Inc. Upper Saddle River, 2011.
2. Oppenheim A. V., Schafer R. W, Buck J. R.: Digital Signal Processing, Prentice-Hall Inc. Upper Saddle River, 1999.
3. Sayood K.: Introduction to Data Compression, Third Edition. Morgan Kaufmann Publishers, San Francisco, 2006.
Modified by dr hab. inż. Andrzej Janczak, prof. UZ (last modification: 11-04-2022 11:58)
