Pengkodean dan Kompresi Data
Learn data compression basics! Explore information theory, lossless coding, information quantity, and entropy. Understand data efficiency.
Course Content
Pengkodean dan Kompresi Data is an all-levels Udemy course taught by Telkom University. It runs 5h 30m, is taught in English, and includes a certificate of completion. Learners rate it 5.0 out of 5 from 1 review, with 3 students enrolled. It is a paid course, first published February 2023.
This course provides a comprehensive introduction to data compression techniques, covering fundamental information theory concepts and a range of lossless and lossy compression methods. You'll gain a strong understanding of how to reduce data size without sacrificing quality (lossless) and the trade-offs involved in achieving higher compression ratios (lossy).
Course Content Highlights:
- Foundations of Information Theory: Explore core concepts like information quantity, entropy, and the Kraft-McMillan Inequality. Learn about prefix-free codes, essential for efficient data representation.
- Lossless Data Compression: Delve into various lossless compression techniques, including:
- Huffman Coding: A widely used statistical coding method.
- Golomb and Tunstall Codes: Advanced coding techniques for specific data distributions.
- Arithmetic Coding: A powerful method for achieving high compression ratios.
- Dictionary Techniques: Utilizing dictionaries to represent frequently occurring data.
- Run-Length Encoding (RLE): Efficient compression for repetitive data.
- Block-Wise Transform (BW Transform): Data transformation for compression.
- Introduction to Lossy Data Compression: Understand the principles behind lossy compression, where some data is discarded to achieve greater compression.
- Scalar and Vector Quantization: Learn about scalar quantization and vector quantization applied to both text and image data, crucial for lossy compression.
- Advanced Coding Techniques: Explore Minimum Variance Huffman Coding (MVHC) and Extended Huffman Coding (EHC) for optimized compression.
- Tunstall and Shannon Fano Coding: Learn about alternative coding methods.
- Integer Coding (Golomb): A powerful technique for representing integers efficiently.
Learning Approach & Best Practices:
This course emphasizes a strong foundation in data compression and coding theory. Each compression algorithm will be discussed with a focus on the underlying principles. Regular exercises and independent assignments are included to reinforce understanding and practical application of the concepts.
Recommended Resources:
- Adam Drozdek, Elements of Data Compression, Thomson Brooks/Cole, 2002
- Khalid Sayood, Introduction to Data Compression, Academic Press, 2000
- T.M. Cover, J.A. Thomas, Elements of Information Theory, John Wiley&Sons
- M. Nelson and J.‐L. Gailly. The Data Compression Book. M&T Books, CA, 1996.
- D. Salomon. Data Compression: The Complete Reference. Springer, 1998
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