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