SylabUZ
| Course name | Digital signal processing and compression |
| Course ID | 11.3-WE-INFD-DSPaC-Er |
| Faculty | Faculty of Engineering and Technical Sciences |
| Field of study | WIEiA - oferta ERASMUS / Informatics |
| Education profile | - |
| Level of studies | Second-cycle Erasmus programme |
| Beginning semester | winter term 2018/2019 |
| Semester | 2 |
| ECTS credits to win | 6 |
| 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 |
Basic knowledge on linear causal time-invariant (LTI) systems and fundamental of spectral analysis and filtration of discrete signals.
Skills and competences in FIR and IFIR digital filters design.
Skills and competences in both lossless compression and lossy compression.
Continuous-time and digital-time signal representation. Linear causal time-invariant (LTI) systems. Signal sampling and quantization. Nyquist–Shannon sampling theorem.
Fourier transform. Discrete Fourier transform (DFT) and Fast Fourier transform (FFT). Frequency analysis of signals using DFT.
Z-transform definition and its properties. The transfer function.
Digital filters. Finite impulse response filters (FIR). FIR filters design techniques. Infinite impulse response filters (IFIR). IFIR filters design techniques.
Finite-precision numerical effects in digital signal processing.
Lossless compression. Mathematical preliminaries for lossless compression. Huffman coding. Arithmetic coding. Dictionary coding techniques. Context-based compression. Lossless image compression.
Lossy coding. Mathematical preliminaries for lossy compression. Scalar quantization. Vector quantization. Differential encoding. Transform coding. Karhunen-Loéve transform. Discrete cosine transform. Discrete sine transform. Discrete Walsh-Hadamard transform. Subband coding. Wavelet-based compression.
Audio coding. Speech compression. Image compression. Video compression.
Lecture: classical lecture
Labs: laboratory exercises
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture - the passing condition is to obtain a positive mark from the final test.
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester. as well as give back all reports from laboratory exercises.
Final grade = lecture: 50% + laboratory: 50%
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 (last modification: 14-04-2018 10:49)
