New Clustering Algorithm for Vector Quantization using Rotation of Error Vector

Abstract —The paper presents new clustering algorithm. The proposed algorithm gives less distortion as compared to well known Linde Buzo Gray (LBG) algorithm and Kekre’s Proportionate Error (KPE) Algorithm. Constant error is added every time to split the clusters in LBG, resulting in formation of cluster in one direction which is 135 0 in 2-dimensional case. Because of this reason clustering is inefficient resulting in high MSE in LBG. To overcome this drawback of LBG proportionate error is added to change the cluster orientation in KPE. Though the cluster orientation in KPE is changed its variation is limited to ± 45 0 over 135 . The proposed algorithm takes care of this problem by introducing new orientation every time to split the clusters. The proposed method reduces PSNR by 2db to 5db for codebook size 128 to 1024 with respect to LBG. Keywords-component; Vector Quantization; Codebook; Codevector; Encoding; Compression. I. I NTRODUCTION Exhaustive Search (ES) method gives the optimal result at the World Wide Web Applications have extensively grown since last few decades and it has become requisite tool for education, communication, industry, amusement etc. All these applications are multimedia-based applications consisting of images and videos. Images/videos require enormous volume of data items, creating a serious problem as they need higher channel bandwidth for efficient transmission. Further high degree of redundancies is observed in digital images. Thus the need for image compression arises for resourceful storage and transmission. Image compression is classified into two categories, lossless image compression and lossy image compression technique. Vector quantization (VQ) is one of the lossy data compression techniques[1], [2] and has been used in number of applications, like pattern recognition [3], speech recognition and face detection [4], [5], image segmentation [6-9], speech data compression [10], Content Based Image Retrieval (CBIR) [11], [12], Face recognition[13], [14] iris recognition[15], tumor detection in mammography images [29] etc. VQ is a mapping function which maps k-dimensional vector space to a finite set CB = {C