Course Outline

Introduction: Compression Techniques, Modeling and Coding

Mathematical Preliminaries for Lossless Compression Overview, Introduction to Information Theory, Models, Coding

Huffman Coding, Overview, The Huffman Coding Algorithm, Minimum Variance Huffman Codes, Adaptive Huffman Coding, Application of Huffman Coding

Arithmetic Coding, Overview, Introduction, Coding a Sequence, Generating a Binary Code, Comparison of Huffman and Arithmetic Coding, Applications

Dictionary Techniques Overview, Introduction, Static Dictionary, Adaptive Dictionary, Applications

Mathematical Preliminaries for Lossy Coding Overview, Introduction, Distortion Criteria, Models

Scalar Quantization: Overview, Introduction, The Quantization Problem, Uniform Quantizer, Adaptive Quantization, Non-uniform Quantization

Vector Quantization: Overview, Introduction, Advantages of Vector Quantization over Scalar Quantization

Transform Coding, Overview, Introduction, The Transform, Transforms of Interest, Discrete Cosine Transform, Quantization and Coding of Transform Coefficients, Application to Image Compression – JPEG, Application to Audio Compression,

Wavelet-Based Compression, Image Compression, Embedded Zero-tree Coder, Set Partitioning in Hierarchical Trees, JPEG 2000

Self Learning Component

To be decided by course coordinator at the beginning of semester, which will be a blend of one or more of the e-Learning Resources, Video Lectures, Online courses, tools, research material, web links etc. along with the related assessment component(s).

Laboratory Work

Above concepts are to be implemented and at least 5 experiments are to be carried out.

References

  1. Introduction to Data Compression –By Khalid Sayood Morgan Kaufmann, Harcourt India Publication

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