Image compression with variable block size segmentation

1992 ◽  
Vol 40 (8) ◽  
pp. 2040-2060 ◽  
Author(s):  
J. Vaisey ◽  
A. Gersho
Keyword(s):  
Image Compression ◽  
Block Size ◽  
Variable Block

Image Compression Using Vector Quantization with Variable Block Size Division

2010 ◽  
Vol 130 (8) ◽  
pp. 1431-1439 ◽  
Author(s):  
Hiroki Matsumoto ◽  
Fumito Kichikawa ◽  
Kazuya Sasazaki ◽  
Junji Maeda ◽  
Yukinori Suzuki
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Block Size ◽  
Variable Block

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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