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Lecturer(s)
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Course content
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1. Introduction to advanced multimedia processing 2. Image representation in the digital environment - image as signal, compression 3. Advanced image processing methods - convolution, OCR, object detection 4. Vector image processing - formats, generation, conversion 5. Digital audio processing - sound as a signal, visualization, noise reduction 6. Advanced audio analysis and synthesis techniques - generation, speech, media conversion 7. Video processing - generation, object detection, transmission optimization 8. Text and hypertext processing - formats, analysis, information extraction 9. Hypertext and WWW - interactivity, adaptive content 10. Machine learning and AI in multimedia processing - multimedia analysis 11. Automated multimedia processing - libraries, tools, trends 12. Security and ethical aspects of multimedia 13. Presentation of semester projects 14. Excursions - visit professional workplaces for multimedia The exercises will be directly related to the content of the lectures and will be practically oriented. The acquired knowledge will be verified using real examples and problems using a team approach and current software products for generating, processing, and presenting multimedia.
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Learning activities and teaching methods
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- Class attendance
- 39 hours per semester
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Learning outcomes
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The course aims to provide students with in-depth technical knowledge and practical skills in advanced multimedia processing (image, audio, video, text, and hypertext). Classical methods of multimedia processing will also be presented. Still, the emphasis will be on the state-of-the-art - automation of processing and advanced techniques and machine learning applications in multimedia work. Thus, students will acquire knowledge and skills that will enable them to understand the principles of digital representation of multimedia and learn to effectively use advanced techniques and applications to work with them in solving independent projects.
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Prerequisites
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Knowledge of programming in any language (ideally Python).
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Assessment methods and criteria
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unspecified
Requirements on student: To complete the course, achieving the set limit for obtaining credit by solving continuous tasks, defending your semester project, and passing the theoretical test in the combined examination is necessary.
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Recommended literature
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