Comparison of Lossless Image Compression Techniques based on Context Modeling

ยท GRIN Verlag
แƒ”แƒšแƒฌแƒ˜แƒ’แƒœแƒ˜
79
แƒ’แƒ•แƒ”แƒ แƒ“แƒ˜
แƒ›แƒ˜แƒกแƒแƒฆแƒ”แƒ‘แƒ˜
แƒ แƒ”แƒ˜แƒขแƒ˜แƒœแƒ’แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒ›แƒ˜แƒ›แƒแƒฎแƒ˜แƒšแƒ•แƒ”แƒ‘แƒ˜ แƒ“แƒแƒฃแƒ“แƒแƒกแƒขแƒฃแƒ แƒ”แƒ‘แƒ”แƒšแƒ˜แƒ ย แƒจแƒ”แƒ˜แƒขแƒงแƒ•แƒ”แƒ— แƒ›แƒ”แƒขแƒ˜

แƒแƒ› แƒ”แƒšแƒฌแƒ˜แƒ’แƒœแƒ˜แƒก แƒจแƒ”แƒกแƒแƒฎแƒ”แƒ‘

Master's Thesis from the year 2014 in the subject Computer Science - Software, , course: Image Processing, language: English, abstract: In this thesis various methods for lossless compression of source image data are analyzed and discussed. The main focus in this work is lossless compression algorithms based on context modeling using tree structure. We are going to compare CALIC, GCT-I algorithms to the JPEG2000 standard algorithm which will be considered the reference of comparison. This work includes research on how to modify CALIC algorithm in continuous-tone mode by truncating tails of the error histogram which may lead to improve CALIC compression performance. Also, we are going to propose a modification to CALIC in binary mode by eliminating error feedback mechanism. As when any pixel to be encoded has a different grey level than any of the neighboring pixels, CALIC triggers an escape sequence that switches the algorithm from binary mode to continuous-tone mode. Which means in this case the pixel will be treated as if it was in continuous-tone region. This minor modification should improve CALIC performance in binary images. Finally, we are going to discuss the GCT-I on medical images and compare results to the JPEG2000 standard.

แƒแƒ•แƒขแƒแƒ แƒ˜แƒก แƒจแƒ”แƒกแƒแƒฎแƒ”แƒ‘

Mohamed El-Ghoboushi received his B.E. in Electronics and Electrical communications Engineering from Cairo University, Giza, Egypt, in 2007, and a M.S. degree in Electronics and Electrical communications Engineering from Cairo University, Giza, Egypt, in 2014, He received the Ph.D. degree in electrical communications engineering from Suez Canal University, Ismailia, Egypt, in 2018

แƒจแƒ”แƒแƒคแƒแƒกแƒ”แƒ— แƒ”แƒก แƒ”แƒšแƒฌแƒ˜แƒ’แƒœแƒ˜

แƒ’แƒ•แƒ˜แƒ—แƒฎแƒแƒ แƒ˜แƒ— แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒแƒ–แƒ แƒ˜.

แƒ˜แƒœแƒคแƒแƒ แƒ›แƒแƒชแƒ˜แƒ แƒฌแƒแƒ™แƒ˜แƒ—แƒฎแƒ•แƒแƒกแƒ—แƒแƒœ แƒ“แƒแƒ™แƒแƒ•แƒจแƒ˜แƒ แƒ”แƒ‘แƒ˜แƒ—

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แƒšแƒ”แƒžแƒขแƒแƒžแƒ”แƒ‘แƒ˜ แƒ“แƒ แƒ™แƒแƒ›แƒžแƒ˜แƒฃแƒขแƒ”แƒ แƒ”แƒ‘แƒ˜
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แƒฃแƒ™แƒแƒœแƒแƒœแƒ แƒ™แƒแƒœแƒขแƒ”แƒœแƒขแƒ˜แƒก แƒจแƒ”แƒกแƒแƒฎแƒ”แƒ‘ แƒ›แƒแƒฎแƒกแƒ”แƒœแƒ”แƒ‘แƒ

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