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An Image Compression Algorithm Based on PCA and JL

Zhaodi Xiao
School of Sciences, South China University of Technology, Guangzhou, China
Foshan Power Supply Bureau, Guangdong Power Grid Corp, Foshan, China
Abstract—Principal Components Analysis (PCA) is one of the most frequently used dimensionality reduction methods. PCA is suitable in time-critical case (i.e., when distance calculations involving only a few dimensions can be afforded) [1]. When it comes to image compression, PCA has its significant advantages: good performance in removing of correlations, and high compression ratio. Johnson-Lindenstrauss Lemma is a probability method leading to a deterministic statement of dimensionality reduction. This paper proposes a image compression algorithm: PCA for image compression based on improved Johnson-Lindenstrauss Lemma. 

Index Terms—PCA, johnson-lindenstrauss lemma, image compression, euclidean distances preserving, dimensionality reduction

Cite: Zhaodi Xiao, "An Image Compression Algorithm Based on PCA and JL," Journal of Image and Graphics, Vol. 1, No. 3, pp. 114-116, September 2013. doi: 10.12720/joig.1.3.114-116
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