Course

Unit 1

Information Theory Foundations and Basics of Compression

Information Theory Foundations:  Entropy and its properties; Conditional entropy; Mutual information; Types of codes; Kraft–McMillan Inequality theorem; Source coding theorem. Introduction to Compression Techniques: Need for compression; Types of compression – Lossy and Lossless; Performance measures (Compression ratio, Bit rate, Distortion); Modeling and Coding principles.

Unit 2

Lossless Text Compression Techniques

Text Compression Methods: Huffman Coding – Static and Dynamic; Applications in text compression; Shannon–Fano–Elias Coding; Arithmetic Coding; Dictionary-based Coding – Static and Adaptive methods; LZ family basics; UNIX Compress.

Unit 3

Quantization and Audio Compression

Scalar Quantization: Introduction; Uniform quantization; Adaptive quantization. Vector Quantization:  Introduction; Advantages; LBG Algorithm; Tree-Structured Vector Quantization; Trellis-Coded Quantization. Audio Compression: Distortion criteria – Auditory perception; PCM, DPCM, ADPCM; Predictive coding – Basic algorithm; Basic Sub-band coding; MPEG Audio Coding.

Unit 4

Image Compression

Distortion criteria – Human Visual System (HVS); Transform Coding – Discrete Cosine Transform (DCT); JPEG Standard; JBIG II; GIF Compression; Wavelet-based Compression – Wavelets, Scaling function, Haar Transform; JPEG-2000 Standard.

Unit 5

UNIT V – Video Compression

Motion Estimation and Compensation; Full Search and Fast Search Algorithms; Video Compression Standards – H.261; MPEG-1; MPEG-2; MPEG-4; MPEG-7; Overview of video coding framework and performance considerations.

Text Books / References

Text Books:

1. Khalid Sayood, Introduction to Data Compression, 5th Edition, Morgan Kaufmann / Elsevier, 2018.

References

1. Iain E. G. Richardson, Video Codec Design: Developing Image and Video Compression Systems, Wiley, 2002.
2. David Salomon, Data Compression: The Complete Reference, 4th Edition, Springer, 2007.
3. Allen Gersho and Robert M. Gray, Vector Quantization and Signal Compression, Springer, 1992.

This course introduces the theoretical foundations and practical techniques of data compression for text, audio, image, and video signals. It begins with information theory concepts such as entropy, mutual information, and source coding theorems, and progresses to lossless and lossy compression techniques. The course covers classical coding schemes, quantization methods, transform-based image compression, audio coding standards, and modern video compression techniques. Students will gain analytical skills to evaluate compression performance and practical understanding of multimedia coding standards such as JPEG, JPEG-2000, MPEG, and H.261.

Course Outcomes: After successful completion of the course, students will be able to

• CO1: Explain entropy, mutual information, source coding theorem, and Kraft–McMillan inequality, and relate them to coding efficiency.
• CO2: Apply text compression techniques such as Huffman coding (static/dynamic), Shannon-Fano-Elias coding, arithmetic coding, and dictionary-based coding.
• CO3: Analyze scalar and vector quantization techniques and evaluate audio compression methods such as PCM, DPCM, ADPCM, and MPEG audio coding.
• CO4: Evaluate transform-based and wavelet-based image compression methods including DCT, JPEG, JPEG-2000, JBIG II, GIF, and Haar transforms.
• CO5: Analyze motion estimation and compensation algorithms and compare video compression standards such as H.261, MPEG-1, MPEG-2, MPEG-4, and MPEG-7.

CO-PO Mapping: