New Clustering Algorithm for Vector Quantization using Rotation of Error Vector

TL;DR

This paper introduces a novel clustering algorithm for vector quantization that improves upon existing methods by dynamically adjusting cluster orientations, resulting in lower distortion and better codebook quality.

Contribution

The proposed algorithm innovatively rotates the error vector during cluster splitting, reducing mean squared error compared to LBG and KPE algorithms.

Findings

Reduces PSNR by 2dB to 5dB for codebook sizes 128 to 1024.

Achieves lower distortion than LBG and KPE algorithms.

Effectively changes cluster orientation to improve clustering efficiency.

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 1350 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 +/- 450 over 1350. 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.