Data Compression and data Retrival
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Data Compression and data Retrival Syllabus — Unit-wise Content
The official GTU curriculum for Data Compression and data Retrival (subject code 2161603) as prescribed for Information Technology · Semester 6 divides the course into 8 units, covering 16 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (2161603)Unit 1 Compression Techniques
- Lossless Compression
- Lossy Compression
- Measures of Performance
Unit 2 Mathematical Preliminaries for Lossless Compression Models
- Physical Models Probability Models Markov Models Composite Source Model Coding Uniquely Decodable Codes Prefix Codes Algorithmic Information Theory Minimum Description Length Principle
Unit 3 Huffman Coding
- The Huffman Coding Algorithm 41 Minimum Variance Huffman Codes Adaptive Huffman Coding Update Procedure Encoding Procedure Decoding Procedure Golomb Codes Rice Codes Tunstall Codes Applications of Huffman Coding Lossless Image Compression Text Compression Audio Compression
Unit 4 Arithmetic Coding
- Introduction Coding a Sequence Generating a Tag Deciphering the Tag Generating a Binary Code Uniqueness and Efficiency of the Arithmetic Code Algorithm Implementation Integer Implementation Comparison of Huffman and Arithmetic Coding Adaptive Arithmetic Coding
Unit 5 Dictionary Techniques
- Static Dictionary Digram Coding Adaptive Dictionary The LZ77 Approach The LZ78 Approach Applications File Compression—UNIX compress Image Compression—The Graphics Interchange Format (GIF) Image Compression—Portable Network Graphics (PNG) Compression over Modems—V.42 bis
Unit 6 Predictive Coding
- Prediction with Partial match (ppm) The basic algorithm
- The ESCAPE SYMBOL
- Length of context
- The Exclusion Principle
- The Burrows-Wheeler Transform: Move-to-front coding Lossless Image Compression CALIC
- Multi-resolution Approaches Facsimile Encoding Dynamic Markoy Compression
Unit 7 Mathematical Preliminaries for Lossy Coding
- Distortion criteria
- The Quantization Problem Uniform Quantizer Adaptive Quantization Forward Adaptive Quantization Backward Adaptive Quantization Nonuniform Quantization pdf-Optimized Quantization Companded Quantization
Unit 8 Vector Quantization
- Advantages of Vector Quantization over Scalar Quantization The Linde-Buzo-Gray Algorithm
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