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Lecturer(s)
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Course content
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Course content and syllabus: 1. Introduction - multimedia and their types and classification 2. Text as a medium - text quality and basics of typography, appearance as support for communication 3. Signal digitization - sampling and quantization, AD/DA converters, digitization errors (moiré, noise) 4. Audio data formats, lossless compression, audio transformations 5. Fourier transform in audio, frequency and temporal masking, lossy compression algorithms 6. Higher-level audio processing - commonly used applications, use of artificial intelligence, TTS and STT 7. Raster graphics processing - color palette optimization, basic adjustments, convolution transformations, lossless compression algorithms 8. Lossy compression algorithms and discrete cosine transform 9. Higher-level raster graphics processing - applications, services, AI 10. Vector graphics formats, SVG, principles of XML processing 11. Video processing - video formats, encoding and compression, video streaming and suitable codecs 12. Working with video - software tools, use of AI, creation of multimedia products 13. Excursion to organizations focused on multimedia processing Exercise content: 1. - 2. Classification and examples of multimedia around us, text processing in Python, embedding generation. 3. - 4. Raster image - image editors, histogram, transformation and optimization in Python, frequency analysis. 5. Vector graphics - editing SVG files in Inkscape, parsing XML structure, format conversion. 6. - 8. Sound - importing to PC, frequency analysis, transformation, tools for working with sound, MIDI, and sound synthesis. 9. - 10. Working with video - acquisition, transformation, comparison of codecs, streaming, encoding. 11. AI and multimedia - using AI models to generate multimedia, media conversion (text2image, etc.) HuggingFace models for audio generation, impact on ethics and trust. 12. Media project - complete production process - acquisition, post-processing, distribution via media services. 13. Excursion to a professional workplace.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Demonstration, Practical training, Group work
- Class attendance
- 56 hours per semester
- Preparation for exam
- 26 hours per semester
- Preparation for classes
- 22 hours per semester
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Learning outcomes
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The course is focused on the processing of multimedia signals and data. Its aim is to present advanced techniques that are not presented to the common multimedia user, but are crucial for working with them and can be also applicated to other tasks. The lectures will discuss the main topics dealing with signal processing in general, working with sound, static image and video. During the exercises, students will use discussed procedures practically with the help of available tools, their own implementation of selected methods, or the use of prepared solutions in larger projects. After completing the course the student will gain the knowledge necessary for qualified work with multimedia data, both in theoretical and practical level.
After completing the course, the student will acquire the knowledge necessary for qualified work with multimedia data, both on the theoretical and practical level. Students will be able to process this data at a higher level into multimedia products and at a lower level focused on particular data representation and manipulation with it.
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Prerequisites
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A prerequisite for this course is successful completion of the course OOP I.
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Assessment methods and criteria
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Written examination, Analysis of student's work activities (technical works), Seminar work, Interim evaluation
To successfully complete the course, it is necessary to solve continuous tasks in exercises, pass a theoretical test, develop a semester project. During both regular and make-up credit terms, as well as at every exam session, all aids are prohibited except those permitted by the instructor.
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Recommended literature
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A ABD EL-LATIF, Ahmed, et al. (ed.). AI Techniques for Multimedia Data Processing. IGI Global. 2025.
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BANERJEE, Sreeparna. Elements of Multimedia. CRC Press, 2019.. ISBN 9780429781292.
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GUAN, Ling (ed.). Multimedia image and video processing. CRC press, 2017. ISBN 9781439830871.
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CHRISTENSEN, Mads G. Introduction to Audio Processing. Springer, 2019. ISBN 9783030117818.
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PAREKH, Ranjan. Principles of multimedia. CRC Press. 2025.
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SCHAFFER, Curt. Deep Learning with Python. Shelter Island: Manning Publications, 2021. ISBN 9781617296864.
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ZHANG, Yu-Jin. Image Engineering. Springer Nature Singapore Pte Ltd. 2021. ISBN 978-981-15-5872-6.
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