Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Codebook Generation for Vector Quantization with Edge Features


Affiliations
1 Department of Computer Science and Applications, Gadhigram Rural Institute, Gandhigram – 624302, Tamil Nadu, India
2 Department of Computer Science, Mother Teresa Women’s University, Kodaikanal – 624 102, Tamil Nadu, India
     

   Subscribe/Renew Journal


In this paper, we propose a new technique to retain the edges of the images while compressing them. The image is divided into small blocks of size 4 x 4 pixels. From the list of N image blocks, M (M < N and M is the desired size of codebook) representative blocks are selected to form the codebook. In ordinary codebook generation, all the image blocks are treated equally. But, in the proposed method, the image blocks are classified into high detail blocks and low detail blocks. The high detail blocks represent the edges and the low detail blocks represent the shaded blocks. The high detail blocks are given preference while generating the codebook. Categorization of image blocks improves the quality of the reconstructed images. Codebooks of sizes (M) 128 and 256 are created with the images of size 256 x 256 pixels. The proposed method gives better performance when compared with the results of few existing techniques.


Keywords

Vector Quantization, Training Vector, Code Vector, Codebook, Compression, Edge Block, Shade Block.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 220

PDF Views: 3




  • Codebook Generation for Vector Quantization with Edge Features

Abstract Views: 220  |  PDF Views: 3

Authors

K. Somasundaram
Department of Computer Science and Applications, Gadhigram Rural Institute, Gandhigram – 624302, Tamil Nadu, India
S. Vimala
Department of Computer Science, Mother Teresa Women’s University, Kodaikanal – 624 102, Tamil Nadu, India

Abstract


In this paper, we propose a new technique to retain the edges of the images while compressing them. The image is divided into small blocks of size 4 x 4 pixels. From the list of N image blocks, M (M < N and M is the desired size of codebook) representative blocks are selected to form the codebook. In ordinary codebook generation, all the image blocks are treated equally. But, in the proposed method, the image blocks are classified into high detail blocks and low detail blocks. The high detail blocks represent the edges and the low detail blocks represent the shaded blocks. The high detail blocks are given preference while generating the codebook. Categorization of image blocks improves the quality of the reconstructed images. Codebooks of sizes (M) 128 and 256 are created with the images of size 256 x 256 pixels. The proposed method gives better performance when compared with the results of few existing techniques.


Keywords


Vector Quantization, Training Vector, Code Vector, Codebook, Compression, Edge Block, Shade Block.