SylabUZ
| Nazwa przedmiotu | Digital signal processing and compression |
| Kod przedmiotu | 11.3-WE-INFD-DSPaC-Er |
| Wydział | Wydział Nauk Inżynieryjno-Technicznych |
| Kierunek | Informatyka |
| Profil | ogólnoakademicki |
| Rodzaj studiów | Program Erasmus drugiego stopnia |
| Semestr rozpoczęcia | semestr zimowy 2023/2024 |
| Semestr | 2 |
| Liczba punktów ECTS do zdobycia | 6 |
| Typ przedmiotu | obowiązkowy |
| Język nauczania | angielski |
| Sylabus opracował |
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| Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
| Wykład | 30 | 2 | - | - | Egzamin |
| Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
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
| Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
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.
Zmodyfikowane przez dr hab. inż. Andrzej Janczak, prof. UZ (ostatnia modyfikacja: 04-04-2023 09:30)
