Bat Optimization Based Vector Quantization Algorithm for Medical Image Compression

2018 ◽  
pp. 29-54
Author(s):  
A. Lenin Fred ◽  
S. N. Kumar ◽  
H. Ajay Kumar ◽  
W. Abisha
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Medical Image ◽  
Medical Image Compression

Medical image compression using vector quantization and system error compression

2015 ◽  
Vol 10 (5) ◽  
pp. 554-566 ◽  
Author(s):  
Tanasak Phanprasit ◽  
Kazuhiko Hamamoto ◽  
Manas Sangworasil ◽  
Chuchart Pintavirooj
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Medical Image ◽  
Medical Image Compression ◽  
System Error

Variable rate vector quantization for medical image compression

1990 ◽  
Vol 9 (3) ◽  
pp. 290-298 ◽  
Author(s):  
E.A. Riskin ◽  
T. Lookabaugh ◽  
P.A. Chou ◽  
R.M. Gray
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Medical Image ◽  
Variable Rate ◽  
Medical Image Compression ◽  
Rate Vector

scholarly journals Medical image compression and vector quantization

1998 ◽  
Vol 13 (1) ◽  
pp. 30-53 ◽  
Author(s):  
Sharon M. Perlmutter ◽  
Pamela C. Cosman ◽  
Chien-Wen Tseng ◽  
Richard A. Olshen ◽  
Robert M. Gray ◽  
...  
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Medical Image ◽  
Medical Image Compression

scholarly journals Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Huiyan Jiang ◽  
Zhiyuan Ma ◽  
Yang Hu ◽  
Benqiang Yang ◽  
Libo Zhang
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Compression Ratio ◽  
Medical Image ◽  
Block Size ◽  
Wavelet Coefficients ◽  
Medical Image Compression ◽  
Compression Performance ◽  
Related Information ◽  
Variable Block

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modifiedK-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

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