compress - # 12 ECE 253a Digital Image Processing Pamela...

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Unformatted text preview: # 12 ECE 253a Digital Image Processing Pamela Cosman 11/4/11 Introductory material for image compression Motivation: Low-resolution color image: 512 512 pixels/color, 24 bits/pixel 3/4 MB 3000 2000 pixels, 24 bits/pixel 18 MB Video SDTV: 640 480 pixels 24 bits/pixel color 30 images/sec = 221 Mbps HDTV: 1280 720 pixels 24 bits/pixel color 60 images/sec = 1.3 Gbps 3D Stereo, Multiview, High-dynamic range ...? Why can we compress images? 3 types of redundancy: 1. Coding redundancy: Not everything is equally probable 2. Interpixel redundancy: there are correlations between neighboring pixels, between color planes, between successive frames temporally 3. Psychovisual redundancy: the human visual system doesnt see everything anyway General system for image compression: A compression system typically consists of one or more of the following operations, which may be combined with each other or with additional signal processing: Original Image a45 Signal Decomposition a45 Quantization a45 Lossless Coding a45 Compressed Bit Stream Signal decomposition: the image is transformed into another space, or decomposed into a col- lection of images. Typically this is done by linear transformation by a Fourier or discrete cosine transform or by wavelet or subband filtering. Quantization : analog or high rate digital pixels are converted into a relatively small number of bits. This operation is lossy as it is nonlinear and noninvertible, so information is lost. The conversion can operate on individual pixels (scalar quantization) or groups of pixels (vector quantization). Lossless coding: Binary words are chosen to represent whatever symbols come out of the previous step. Huffman coding is an example of lossless coding. This step is invertible. It is also called entropy coding. Well look at each of these operations in turn, starting with ......
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compress - # 12 ECE 253a Digital Image Processing Pamela...

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