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A New Case for Image Compression Using Logic Function Minimization
Journal article

A New Case for Image Compression Using Logic Function Minimization

Behrouz Zolfaghari and Hamed Sheidaeian
The International journal of Multimedia & Its Applications, Vol.3(2), pp.45-62
05/31/2011
DOI: https://doi.org/10.5121/ijma.2011.3204

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Abstract

Sum of minterms is a canonical form for representing logic functions. There are classical methods such as Karnaugh map or Quine–McCluskey tabulation for minimizing a sum of products. This minimization reduces the minterms to smaller products called implicants. If minterms are represented by bit strings, the bit strings shrink through the minimization process. This can be considered as a kind of data compression provided that there is a way for retrieving the original bit strings from the compressed strings. This paper proposes implements and evaluates an image compression method called YALMIC (Yet Another Logic Minimization Based Image Compression) which depends on logic function minimization. This method considers adjacent pixels of the image as disjointed minterms constructing a logic function and compresses the 24-bit color images through minimizing the function. We compare the compression ratio of the proposed method to those of existing methods and show that YALMIC improves the compression ratio by about 25% on average.

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