Open AccessEnhanced side match vector quantisation based on constructing complementary state codebook
Author(s) -
Ma Xiaoxiao,
Pan Zhibin,
Hu Sen,
Wang Lingfei
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0125
Subject(s) - codebook , encoding (memory) , image (mathematics) , vector quantization , image quality , artificial intelligence , quality (philosophy) , computer science , image compression , pattern recognition (psychology) , mathematics , computer vision , image processing , philosophy , epistemology
Side match vector quantisation (SMVQ) technique has been widely used in a lot of image compression and data hiding applications. It can effectively decrease the bit rate (BR) but the encoding visual quality of the image using SMVQ is generally poor because the correlation among neighbouring image blocks is still low. In this study, the authors propose a new enhanced SMVQ (ESMVQ) by introducing the concept of complementary state codebook (CSC) to improve the visual quality of SMVQ. Encoding input vectors by using CSC, ESMVQ can achieve almost the same encoding visual quality as using the conventional vector quantisation. As a result, the encoding visual quality of image can be significantly improved by exploiting the power of CSC. Experimental results show that the improvement of proposed ESMVQ method over SMVQ is up to 4.677 dB at a similar BR for image Lena when the main codebook size is 1024.