Lec6 - IEG 4190 Lecture Notes 6; Lecturer: Jianzhuang Liu...

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1 IEG 4190 Lecture Notes 6; Lecturer: Jianzhuang Liu Outline: 1. Introduction to Image Coding 2. Scalar Quantization 3. Vector Quantization 4. Huffman Coding
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2 Introduction to Image Coding Objective of image coding Representation of an image with acceptable quality using as few bits as possible. Image coder Image source image Image decoder Channel decoder Channel Transimitter Receiver Channel encoder Reconstructed
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3 Introduction to Image Coding, cont’d Applications of image coding: Reduction of channel bandwidth for image transmission Digital television, video conferencing, facsimile, etc. Reduction of storage requirement VCD, DVD, Digital Video Tape, etc.
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4 Introduction to Image Coding, cont’d Issues in image coding: Transformation Image source string of bits Quantization Codeword assignment What to code (Transformation) Examples: image intensity, image transform coefficients, image model parameters. How to assign reconstruction levels (Quantization) Examples: Uniform spacing between reconstruction levels, Nonuniform spacing between reconstruction levels. Codeword assignment Examples: Uniform-length codeword assignment Variable-length codeword assignment
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5 Introduction to Image Coding, cont’d Transformation Image source string of bits Quantization Codeword assignment All these three elements try to exploit Redundancy in image source Limitation of display device Limitation of human visual system The three elements are closely interrelated
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6 Scalar Quantization To represent a continuous scalar value f with a finite number of bits, only a finite number of quantization levels L can be used. If each scalar is quantized independently, the procedure is called scalar quantization. Uniform quantization: equal spacing of reconstruction levels.
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Lec6 - IEG 4190 Lecture Notes 6; Lecturer: Jianzhuang Liu...

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