k‐PCA: a semi‐universal encoder for image compression

Abstract

Purpose

In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used for reconstructing the original data, PCA has not been successfully applied to image compression. To solve this problem, this paper aims to propose a new technique called k‐PCA.

Design/methodology/approach

Actually, k‐PCA is a combination of vector quantization (VQ) and PCA. The basic idea is to divide the problem space into k clusters using VQ, and then find a PCA encoder for each cluster. The point is that if the k‐PCA encoder is obtained using data containing enough information, it can be used as a semi‐universal encoder to compress all images in a given domain.

Findings

Although a k‐PCA encoder is more complex than a single PCA encoder, the compression ratio can be much higher because the transformation matrices can be excluded from the encoded data. The performance of the k‐PCA encoder can be improved further through learning. For this purpose, this paper‐proposes an extended LBG algorithm.

Originality/value

The effectiveness of the k‐PCA is demonstrated through experiments with several well‐known test images.

Citation

Lv, C. and Zhao, Q. (2007), "k‐PCA: a semi‐universal encoder for image compression", International Journal of Pervasive Computing and Communications, Vol. 3 No. 2, pp. 205-220. https://doi.org/10.1108/17427370710847327