Moduł oferowany także w ramach programów studiów:
Informacje ogólne:
Nazwa:
Advanced Multimedia Information Processing and Communications
Tok studiów:
2014/2015
Kod:
ITE-1-704-s
Wydział:
Informatyki, Elektroniki i Telekomunikacji
Poziom studiów:
Studia I stopnia
Specjalność:
-
Kierunek:
Teleinformatyka
Semestr:
7
Profil kształcenia:
Ogólnoakademicki (A)
Język wykładowy:
Angielski
Forma i tryb studiów:
Stacjonarne
Osoba odpowiedzialna:
dr hab. inż. Leszczuk Mikołaj (leszczuk@agh.edu.pl)
Osoby prowadzące:
dr hab. inż. Leszczuk Mikołaj (leszczuk@agh.edu.pl)
Krótka charakterystyka modułu

The classes will be realised in a computer laboratory. The course consists of computer exercises on multimedia (mainly video) data processing and transmission.

Opis efektów kształcenia dla modułu zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Powiązania z EKK Sposób weryfikacji efektów kształcenia (forma zaliczeń)
Wiedza
M_W001 Knows the the concept of measuring multimedia QoE. TE1A_W18 Wynik testu zaliczeniowego,
Aktywność na zajęciach,
Kolokwium
M_W002 Demonstrates advanced knowledge of signal analysis and processing algorithms, including sound and video. TE1A_W01 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
M_W003 Has sound and advanced knowledge in the area of digital multimedia signal processing and telecommunications. TE1A_W05 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
M_W004 Demonstrates sound and advanced programming knowledge in the area of using image processing libraries. TE1A_W10 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
Umiejętności
M_U001 Is able to use properly chosen methods and tools in order to measure parameters of multimedia QoE subjectively and objectively. TE1A_U16 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
M_U002 Is able to plan and carry out subjective and objective measurements of multimedia QoE as well as to present the results in numerical or graphical form, interpret them and make conclusions. TE1A_U17 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
M_U003 Demonstrates the ability to design an algorithm, is able to use high-level programming languages and other suitable computer tools in order to carry image processing. TE1A_U21 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
M_U004 Is able to perform an advanced analysis and processing of multimedia signals in the time or frequency domain, using digital techniques, as well as appropriate software tools. TE1A_U08 Wynik testu zaliczeniowego,
Kolokwium,
Aktywność na zajęciach
Matryca efektów kształcenia w odniesieniu do form zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Forma zajęć
Wykład
Ćwicz. aud
Ćwicz. lab
Ćw. proj.
Konw.
Zaj. sem.
Zaj. prakt
Zaj. terenowe
Zaj. warsztatowe
Inne
E-learning
Wiedza
M_W001 Knows the the concept of measuring multimedia QoE. - + - - - - - - - - -
M_W002 Demonstrates advanced knowledge of signal analysis and processing algorithms, including sound and video. - - - - - - - - - - -
M_W003 Has sound and advanced knowledge in the area of digital multimedia signal processing and telecommunications. - + - - - - - - - - -
M_W004 Demonstrates sound and advanced programming knowledge in the area of using image processing libraries. - + - - - - - - - - -
Umiejętności
M_U001 Is able to use properly chosen methods and tools in order to measure parameters of multimedia QoE subjectively and objectively. - + - - - - - - - - -
M_U002 Is able to plan and carry out subjective and objective measurements of multimedia QoE as well as to present the results in numerical or graphical form, interpret them and make conclusions. - + - - - - - - - - -
M_U003 Demonstrates the ability to design an algorithm, is able to use high-level programming languages and other suitable computer tools in order to carry image processing. - + - - - - - - - - -
M_U004 Is able to perform an advanced analysis and processing of multimedia signals in the time or frequency domain, using digital techniques, as well as appropriate software tools. - + - - - - - - - - -
Treść modułu zajęć (program wykładów i pozostałych zajęć)
Ćwiczenia audytoryjne:
  1. Logical and Arithmetical Operations on Images

    Logical operations with images (AND, OR, XOR, NEGATIVE, Threshold). Arithmetical/numerical operations on images (Weighted Sum, basics of background modelling).

  2. Histograms

    Equalisation, Matching, Image Statistics. Histograms: generation, comparing and equalisation.

  3. Mathematical Morphological (MM) Operations on Images

    Morphological operations (erode, dilate, hit-or-miss, thickening, distance metrics, contours finding, etc.).

  4. Digital Filters on Images

    Spatial and Frequency Filtering (the difference). Spatial domain filters (smoothing, order-statistic, sharpening, un-sharpening, laplacian). Frequency domain filters (Fourier Transform on images, smoothing, sharpening, homomorphic filtering). Comparison between same filters in spatial and frequency domains. Digital filters on images: spatial and frequency domain filter in use (smooth, sharpness, etc.).

  5. Corner Extraction

    Basic algorithms for corner extraction (e.g. Canny).

  6. Visual Descriptors for Image Classification

    SIFT, SURF, HOG, FAST, etc. Multimedia content libraries, commonly used for application development: MORPH, TREC, etc. Image classification based on visual descriptors such as SIFT, SURF, ORB, MPEG-7, etc.

  7. Basics of Intelligent Multimedia Processing and Analytics – Object Detection

    Examples of simple content-based feature recognition. Object detection based on Haar-Cascades-like features.

  8. Object Tracking

    Basics of Intelligent Multimedia Processing and Analytics. Basic algorithms for simple object tracking.

  9. Test 1

    Test 1

  10. Shot Boundary Detection (SBD)

    Shot Boundary Detection (SBD). Video summarisation.

  11. Theoretical Introduction to Using Image Processing Libraries

    Programming skills in signal processing, image processing and Computer Vision (CV) – libraries (OpenCV, GIL, PCL, etc.). Adaptation of algorithms for own purposes.

  12. Basics of Using Image Processing Libraries

    Programming skills in signal processing, image processing and Computer Vision (CV) – libraries (OpenCV, GIL, PCL, etc.). Adaptation of algorithms for own purposes.

  13. Multimedia Quality of Experience (QoE) – 1

    Business approach and technical aspects. Example multimedia psychophysical experiment.

  14. Multimedia Quality of Experience (QoE) – 2

    Business approach and technical aspects. Example multimedia psychophysical experiment.

  15. Digital watermarking

    Study of the impact of changes in the quality of the image due to modifications related with embedding digital watermark. Extended, most state-of-the-art knowledge in the area of (modern) algorithms used in multimedia processing (digital watermarking).

  16. Test 2

    Test 2.

Nakład pracy studenta (bilans punktów ECTS)
Forma aktywności studenta Obciążenie studenta
Sumaryczne obciążenie pracą studenta 75 godz
Punkty ECTS za moduł 3 ECTS
Samodzielne studiowanie tematyki zajęć 25 godz
Udział w ćwiczeniach audytoryjnych 32 godz
Dodatkowe godziny kontaktowe z nauczycielem 18 godz
Pozostałe informacje
Sposób obliczania oceny końcowej:

To receive a final pass, you must obtain a positive grade, in accordance with the AGH University of Science and Technology. The score is based on the sum of the scores from the colloquium / colloquium, or slightly adjusted by the attendance results and other activities.
The student has the right to pass a correction on the basis of (oral) corrective colloquium.

Wymagania wstępne i dodatkowe:

Knowledge of the subject “Multimedia Information Processing and Communications”

Zalecana literatura i pomoce naukowe:

Provided individually in instructions for exercises.

Publikacje naukowe osób prowadzących zajęcia związane z tematyką modułu:
  1. Mikołaj Leszczuk, Lucjan Janowski, Piotr Romaniak, Zdzisław Papir, September 2013, „Assessing quality of experience for high definition video streaming under diverse packet loss patterns”, Elsevier Signal Processing: Image Communication (SPIC) – IF=1.153, Volume 28, Issue 8, Pages 903-916.
  2. Mikołaj Leszczuk, Lucjan Janowski, 8 February 2015, „Advanced mechanisms for delivering high-quality digital content”, Proceedings of IS&T/SPIE Electronic Imaging (EI) – WoS, Volume 9396, 6 Pages.
  3. Mikołaj Leszczuk, Mateusz Hanusiak, Mylène C. Q. Farias, Emmanuel Wyckens, George Heston, 6 September 2014, „Recent Developments in Visual Quality Monitoring by Key Performance Indicators”, Springer Multimedia Tools and Applications (MTAP) – IF=1.346, Pages 1-23.
  4. Mikołaj Leszczuk, Krzysztof Kowalczyk, Lucjan Janowski, Zdzisław Papir, November 2015, „Lightweight Implementation of No-Reference (NR) Perceptual Quality Assessment of H.264/AVC Compression”, Elsevier Signal Processing: Image Communication (SPIC) – IF=1.462, Volume 39, Part B, Pages 457-465.
  5. Mariusz Duplaga, Mikołaj Leszczuk, Zdzisław Papir, Artur Przelaskowski, 11-12 September 2008, „Evaluation of Quality Retaining Diagnostic Credibility for Surgery Video Recordings”, Proceedings of Microsoft Visual Information Systems (VISUAL) – WoS, Volume 5188, Pages 227-230.
  6. Mariusz Duplaga, Mikołaj Leszczuk, Zdzisław Papir, Artur Przelaskowski, December 2008, „Compression evaluation of surgery video recordings retaining diagnostic credibility (compression evaluation of surgery video)”, Springer Optoelectronics Review (OER) – IF=1.135, Volume 16, Issue 4, Pages 428-438.
  7. Mikołaj Leszczuk, Mariusz Duplaga, 20 December 2011, „Algorithm for Video Summarization of Bronchoscopy Procedures”, Bio-Medical Engineering On-Line (BEO) – IF=1.405, Volume 10.
  8. Mikołaj Leszczuk, Łukasz Skoczylas, Andrzej Dziech, 26-28 September 2013, „Simple Solution for Public Transport Route Number Recognition Based on Visual Information”, Proceedings of Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) – WoS, Pages 32-38.
  9. Mikołaj Leszczuk, Łukasz Dudek, Marcin Witkowski, June 2015, „Classification of video sequences into chosen generalized use classes of target size and lighting level”, Springer Multimedia Tools and Applications (MTAP) – IF=1.346, Volume 74, Issue 12, Pages 4381-4395.
  10. Mikołaj Leszczuk, Lucjan Janowski, Piotr Romaniak, Andrzej Głowacz, Ryszard Mirek, 2-3 June 2011, „Quality Assessment for a Licence Plate Recognition Task Based on a Video Streamed in Limited Networking Conditions”, Proceedings of Multimedia Communications Services and Security (MCSS) – WoS, Volume 149, Pages 10-18.
  11. Mikołaj Leszczuk, Lucjan Janowski, 26-28 September 2013, „Database for Video Quality Assessment in License Plate Recognition”, Proceedings of Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) – WoS, Pages 51-55.
  12. Mikołaj Leszczuk, 2-3 June 2011, „Assessing Task-Based Video Quality – A Journey from Subjective Psycho-Physical Experiments to Objective Quality Models”, Proceedings of Multimedia Communications Services and Security (MCSS) – WoS, Volume 149, Pages 91-99.
  13. Mikołaj Leszczuk, January 2014, „Optimising Task-Based Video Quality”, Multimedia Tools and Applications (MTAP) – IF=1.346, Volume 68, Issue 1, Pages: 41-58.
  14. Mikołaj Leszczuk, Joel Dumke, 6-7 June 2013, „Survey of Recent Developments in Quality Assessment for Target Recognition Video”, Proceedings of Multimedia Communications, Services and Security (MCSS) – WoS, Volume 368, Pages 59-70.
  15. Mikołaj Leszczuk, Artur Koń, Joel Dumke, Lucjan Janowski, 31 May-1 June 2012, „Redefining ITU-T P.912 Recommendation Requirements for Subjects of Quality Assessments in Recognition Tasks”, Proceedings of Multimedia Communications Services and Security (MCSS) – WoS, Volume 287, Pages 188-199.
  16. Mikołaj Leszczuk, January 2015, „Revising and improving the ITU-T Recommendation P.912”, Journal of Telecommunications and Information Technology, 2015 nr 1, Pages 10–14.
  17. Carolyn Ford, Lucjan Janowski, Mikołaj Leszczuk, March 2016, „Subjective video quality assessment methods for recognition tasks”, ITU-T Recommendation P.912.
  18. Video Summarization Framework for Newscasts and Reports–Work in Progress, M Leszczuk, M Grega, A Koźbiał, J Gliwski, K Wasieczko, K Smaïli, Communications in Computer and Information Science … 2017
  19. Detection of Lip Synchronization Artefacts, IB Fernández, M Leszczuk, Communications in Computer and Information Science … 2015
  20. Statistical Assessment of Retrieved Images and Videos Using the INACT Tool, L Michalek, M Grega, M Leszczuk, D Bryk, B Grabowski, R Turon, … Communications in Computer and Information Science … 2014
  21. Practical application of near duplicate detection for image database, A Eshkol, M Grega, M Leszczuk, O Weintraub, Communications in Computer and Information Science … 2014
  22. Public transport vehicle detection based on visual information, M Leszczuk, R Baran, Ł Skoczylas, M Rychlik, P Ślusarczyk, Communications in Computer and Information Science … 2014
  23. Downloading and analysing images from the Internet in order to detect special objects, M Leszczuk, T Piwowarczyk, M Grega, Communications in Computer and Information Science … 2013
Informacje dodatkowe:

The classes will be realised in a computer laboratory.