Vector quantization based image compression using generalized improved fuzzy clustering

Abstract

This paper presents a new approach to vector quantization (VQ) based image compression, which uses an improved partition-based fuzzy clustering algorithm. The proposed algorithm employs a generalized fuzzy c-means clustering approach employing improved fuzzy partitions (called GIFP-FCM) that was proposed as a modification of the classical fuzzy c-means algorithm with an aim to reward crisp membership degrees. This clustering approach, when applied to VQ based image compression, aptly demonstrates that the transition from fuzzy to crisp mode is more efficient compared to the known approaches and is also independent of the choice of the initial codebook vector. The technique is also fast and easy to implement, and has rapid convergence. Several experimental results are presented to demonstrate its distinct advantage over other commonly used algorithms for image compression.