New Clustering Algorithm for Vector Quantization using Walsh Sequence

Abstract

In this paper we present an effective clustering algorithm to generate codebook for vector quantization (VQ). 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 2dimensional 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 0. KEVR introduces new orientation every time to split the clusters. But in KEVR the error vector sequence is the binary representation of numbers, so the cluster orientation change slowly in every iteration. To overcome this drawback we propose the technique which uses Walsh sequence to rotate the error vector. The proposed technique (Kekre's error vector rotation using Walsh-KEVRW) is based on KEVR algorithm. The proposed methodology is tested on different training images for code books of sizes 128, 256, 512, 1024. Our result shows that KEVRW gives less MSE and high PSNR compared to LBG, KPE and KEVR.

Effect of watermarking in vector quantization based image compression

“…One of the most popular clustering [19] algorithms is the LBG algorithm. The LBG clustering algorithm is the simplest and most commonly used in vector quantization.…”

Novel Keyframe Extraction for Video Content Summarization using LBG Codebook Generation Technique of Vector Quantization

“…In other words, Vector Quantization is mapping function that maps k dimensional vector space to finite set CB = {C 1 , C 2 … C N }. The set CB is called a codebook consisting of N number of code vector and each code vector Ci= {c i1 ,c i2 ....c ik } is of K dimension [9]. Here ten different Codebooks sizes like 2, 4, 8, 16, 32, 64, 128, 256, 512 and 1024 are used.…”