Fuzzy block truncation coding

作     者：Boudraa, AO Beghdadi, A Dehalk, SMR Iordache, R 

作者机构：Ecole Navale IRENav F-29200 Brest France Univ Paris 13 F-93430 Villetaneuse France ENST Dept Image F-75634 Paris France Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland 

出 版 物：《OPTICAL ENGINEERING》 (光学工程)

年 卷 期：2002年第41卷第12期

页      面：3161-3167页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 0702[理学-物理学] 

主　　题：image processing image coding block truncation coding fuzzy clustering fuzzy C-means algorithm contextual quantization 

摘      要：Block truncation coding (BTC) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTC is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTC to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTC, economical BTC (EBTC), absolute moment BTC (AMBTC), and a minimum mean square error quantizer (MMSEQ). Computation time required by AMBTC, EBTC, and fuzzy BTC methods are reported. (C) 2002 Society of Photo-Optical Instrumentation Engineers.