AI Summary & Key Takeaways
AI InsightAs a result, it is seen that the proposed new technique performs better according to the criteria of MSE.
📌 3 Key Takeaways
Abstract
Linde-Buzo-Gray (LBG) algorithm is used in image processing for Vector Quantization (VQ). LBG technique is robust, performs locally best but depends on the initial codebook. In the splitting based VQ, the first center is defined as average of all vectors. The rest of 2n centres are calculated by splitting and updated procedure. In the proposed new technique (NLBG) the LBG is improved and insted of splitting all centres into two new areas, the worst area that has highest mean square error splitted and updated into to new areas. Therefore, the number of codevectors is increased one by one apart from the classical LBG. Consequently, the performance of the codebook is increased globally. In this paper, the new technique is applied to the standard images, compared to the FCM(Fuzzy C-Means), K-Means (K – Ortalamalar) and LBG. As a result, it is seen that the proposed new technique performs better according to the criteria of MSE.
Özet
Linde-Buzo-Gray (LBG) algoritması, görüntü sıkıştırmada Vektör Nicemleme (VN) tekniği için kullanılan,