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Lecturer(s)
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Course content
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Course contents and syllabus: 1. Introduction - multimedia and their types and classification 2. Signal digitization - sampling and quantization, AD / DA converters, digitization errors (moire, noise?) 3. Sound processing - formats, lossless compression, sound transformations 4. Sound processing - Fourier transform in sound, frequency and time masking, lossy compression algorithms 5. Raster image processing - color palette optimization, image processing, convolutional transformations, lossless compression algorithms 6. Raster image processing - Fourier transform discrete and continuous, lossy compression algorithms 7. Vector image processing - vector graphics formats, XML processing 8. Video processing - lossy video compression, video streaming, video formats 9. Modern methods of multimedia processing using artificial intelligence Course contents and syllabus: 1. Introduction - multimedia and their types and classification 2. Signal digitization - sampling and quantization, AD / DA converters, digitization errors (moire, noise?) 3. Sound processing - formats, lossless compression, sound transformations 4. Sound processing - Fourier transform in sound, frequency and time masking, lossy compression algorithms 5. Raster image processing - color palette optimization, image processing, convolutional transformations, lossless compression algorithms 6. Raster image processing - Fourier transform discrete and continuous, lossy compression algorithms 7. Vector image processing - vector graphics formats, XML processing 8. Video processing - lossy video compression, video streaming, video formats 9. Modern methods of multimedia processing using artificial intelligence
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, Excursion, Practical training, Case studies
- 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.
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Recommended literature
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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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ZHANG, Yu-Jin. Image Engineering. Springer Nature Singapore Pte Ltd. 2021. ISBN 978-981-15-5872-6.
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