Application of Principal Component Analysis to Image Compression

Wilmar Hernandez and Alfredo Mendez

A chapter in Statistics - Growing Data Sets and Growing Demand for Statistics from IntechOpen

Abstract: In this chapter, an introduction to the basics of principal component analysis (PCA) is given, aimed at presenting PCA applications to image compression. Here, concepts of linear algebra used in PCA are introduced, and PCA theoretical foundations are explained in connection with those concepts. Next, an image is compressed by using different principal components, and concepts such as image dimension reduction and image reconstruction quality are explained. Also, using the almost periodicity of the first principal component, a quality comparative analysis of a compressed image using two and eight principal components is carried out. Finally, a novel construction of principal components by periodicity of principal components has been included, in order to reduce the computational cost for their calculation, although decreasing the accuracy.

Keywords: principal component analysis; population principal components; sample principal components; image compression; image dimension reduction; image reconstruction quality (search for similar items in EconPapers)
JEL-codes: C60 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:143689

DOI: 10.5772/intechopen.75007

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