Syllabus Data Compression - (3171615) CreditsExamination MarksTotal Marks CTheory MarksPractical Marks ESE (E)PA (M)ESE (V)PA (I) 470302030150 1.Introduction : Compression Techniques, Modeling and Coding Mathematical Preliminaries for Lossless Compression : Models - Physical Models, Probability Models, Markov Models Coding - Uniquely Decodable Codes, Prefix codes. (Chapter - 1) 2.Huffman coding : The Huffman Coding Algorithm - 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 Arithmetic coding : Coding a sequence - Generating a Tag, Deciphering the Tag, Generating Binary Code - Uniqueness and Efficiency of the Arithmetic code, Algorithm implementation, Integer Implementation. Comparison of Huffman and Arithmetic coding Applications. (Chapter - 2) 3.Dictionary Techniques : Static Dictionary - Diagram Coding Adaptive Dictionary - The LZ77 approach, The LZ78 Approach Applications - Image compression. (Chapter - 3) 4.Context based Compression : 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. (Chapter - 4) 5.Lossless Image Compression : The Old JPEG Standard, CALIC, JPEG-LS. (Chapter - 5) 6.Mathematical Preliminaries for Lossy Coding : Distortion criteria - The Human Visual System, Auditory Perception Models - Probability Models, Linear System Models, Physical Models. Scalar Quantization : The Quantization Problem Uniform Quantizer Adaptive Quantization - Forward Adaptive, Backward Adaptive Non uniform Quantization - pdf optimized Quantization, Companded Quantization Entropy Coded Quantization - Entropy coding of Lloyd - Max Quantizer Outputs. Vector Quantization : Advantages of Vector Quantization over Scalar Quantization
