Abstract – According to advances in digital imaging technology, interest in high-resolution (HR) images has been increased. Various methods that convert low-resolution (LR) images to HR ones have been presented. In this paper, to reduce the computational load we propose a vector quantization (VQ) based algorithm that reconstructs an interpolation image by adding to an initially interpolated image high-frequency components predicted from training with a number of example image sets. The proposed interpolative classified VQ (ICVQ) algorithm combines interpolative VQ with classified VQ. With a number of (LR and HR) example image sets, we construct two types of (LR and HR) codebooks. Comparative experiments with three conventional image interpolation algorithms show that the proposed interpolation algorithms using ICVQ effectively preserve edges to which the human visual system is sensitive. The proposed algorithm can be applicable to various image- and video-based applications such as digital camera and digital television.