Parallel Approach to Fuzzy Vector Quantization for Image Compression

Fuzzy clustering based vector quantization algorithm has been widely used in the field of data compression since the use of fuzzy clustering analysis in the early stages of a vector quantization process can make this process less sensitive to initialization. However, the process of fuzzy clustering is computationally very intensive because of its complex framework for the quantitative formulation of the uncertainty involved in the training vector space. To overcome the computational burden of the process, we introduce a parallel implementation of Fuzzy Vector Quantization (FVQ) using a representative data parallel architecture which consists of 4,096 processing elements (PEs). Our parallel approach provides a computationally efficient solution with the 4,096 PEs by employing an effective vector assignment strategy for the transition from soft to crisp decisions during the clustering process. Experimental results show that our parallel approach provides 1000times greater performance and 100times higher energy efficiency than other implementations using commercial processors such as ARM families.