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HistogramEqualization - Cell Applications Solutions...

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Cell Applications & Solutions © 2006 IBM Research IBM Confidential 10/03/11 Histogram Equalization with Cell Broadband Engine™
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Cell Applications and Solutions © 2006 IBM Research 2 IBM Confidential 10/03/11 Content Overview: Histogram Equalization Definitions Assumptions, Highlights Approach: Histogram Computation Approach: Transform Image Performance Results
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Cell Applications and Solutions © 2006 IBM Research 3 IBM Confidential 10/03/11 Overview: Histogram Equalization One of the most significant part of Image Processing Improves contrast by redistributing intensity distributions Compute a uniform histogram Three stages: 1. Compute 2. Normalize 3. Transform
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Cell Applications and Solutions © 2006 IBM Research 4 IBM Confidential 10/03/11 Definitions First Stage: Computing the Histogram Parse the input image Count each distinct pixel value in the image Ex. for 8-bit pixels, the Max Pixel Value is 255, and array size is 256. Second Stage: Computing the normalized sum of histogram Store the sum of all the histogram values normalize by multiplying each element by (maximum-pixel- value/number of pixels). Third Stage: Transforming input image into output image Use the normalized array as a look up table for mapping the input image pixel value to the new set of values from stage
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Cell Applications and Solutions © 2006 IBM Research 5 IBM Confidential 10/03/11 Assumptions, Highlights Assumptions for demo: 8-bit color scale Approach Highlights: Parallelize Reduce dependencies Loop unroll SIMDize the code using vectors and SPE intrinsics
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