1. SylabUZ
2. Wydział Informatyki, Elektrotechniki i Automatyki
3. semestr zimowy 2020/2021
4. Informatyka - Program Erasmus drugiego stopnia
5. Digital signal processing and compression

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Digital signal processing and compression - opis przedmiotu

Cel przedmiotu

Acquaintance with the basic knowledge of discrete linear systems, spectral analysis, and filtration of discrete signals. Developing the skill of designing SOI and NOI filters. Acquainted with the basic methods of lossless compression and lossy compression, their properties and applications.

Wymagania wstępne

Zapisz zmiany

Mathematical analysis

Zakres tematyczny

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.

Metody kształcenia

Lecture: traditional lecture
Laboratorium: lab exercises

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

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.

 

Literatura podstawowa

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.

Literatura uzupełniająca

1. Domański M.: Zaawansowane techniki kompresji obrazów i sekwencji wizyjnych. Wyd. Pol. Poznańskiej. Poznań, 1998.

Uwagi

Zmodyfikowane przez prof. dr hab. inż. Andrzej Obuchowicz (ostatnia modyfikacja: 28-04-2020 13:00)