Sub-block interchange for lossless image compression

Ng, Ka-Ho; Cheng, L.

doi:10.1109/30.754441

Cited by 5 publications

(9 citation statements)

References 2 publications

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“…2 and 3 respectively. [22] DPCM and HC 1.64-3.12 Ng and Cheng method [32] Sub-block interchange 1.44-1.52 Chang and Lin method [33] Two-way smaller difference 1.64-1.70 S.Ganget al, method [34] DVQ and SPIHT coding 2.75 Yong et al, method [35] Interval number method 2.12-2.38 Lurawave software [36] Wavelets-LS Row by row classification 6.60 and LZW coding Zhang method [38] Neighborhood block matching 4.54 Weinberger et al Table 3 it can be seen that the achieved compression ratio of the proposed algorithm is equal or better than the compression ratios achieved using existing lossless compression techniques. Moreover, Table 4 shows that the number of bits required to represent each pixel is much less compared with the other methods.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Low Complexity Lossless Coding Algorithm for Medical Images

Ghrare

Ali

Jumari

et al. 2009

American J. of Applied Sciences

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Problem statement:Nowadays a large number of various medical images are generated from hospitals and medical centers with sophisticated image acquisition devices, the movement toward digital images in radiology presents the problem of how to conveniently and economically store, retrieve and transmit the volume of digital images. Thus digital image data compression is necessary in order to solve this problem. So in a wide range of medical applications such as disease diagnostic and during the compression process, the loss of information is unacceptable; hence medical images are required to be at high resolution as possible. Instead of lossy compression with relatively high compression ratio, mathematical lossless compression methods are favored in this field. Approach: In this study, an efficient new lossless image coding algorithm using a simple technique was presented. Our coding algorithm was based on pixel redundancy reduction by formulating two matrices only, which were Gray Scale Matrix (GSM) and Binary Matrix (BM). These matrices had been used for coding and decoding processes. Results: Results showed that the maximum compression ratio achieved using the proposed method was 4:1, which was more efficient than the present lossless techniques, moreover the computational complexity is greatly simplified; therefore producing very fast coding and decoding. Conclusion: This algorithm was most suitable for those images where lossy compression was avoided such as medical images used for teleradiology and other telemedicine purposed and it can be applied to other medical modalities.

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Section: Resultsmentioning

confidence: 99%

An Efficient Low Complexity Lossless Coding Algorithm for Medical Images

Ghrare

Ali

Jumari

et al. 2009

American J. of Applied Sciences

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“…It can cause the increase of unnecessary computation to apply preprocessing to total image in order to draw some necessary information. Therefore, the research on the decrease of computation time by designating a necessary part as a sub-block is being in progress [5][6][7][8][9]. This section explains edge detection based on image processing and central moment that is important when determining position of windows.…”

Section: Decision Of Window Sizementioning

confidence: 99%

“…A neural network has frequently been used in the previous studies on obstacle detection through an input image [9,23,24].…”

Section: Obstacle Recognitionmentioning

confidence: 99%

Applications of moving windows technique to autonomous vehicle navigation

Choi

Lee

2006

Image and Vision Computing

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“…Now a days the performance of entropy coding techniques are very close to its theoretical bound, and thus more research activities concentrate on de-correlation stage. Many algorithms are used for the lossless compression of the images [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Lossless Compression of Digital Mammography Using Fixed Block Segmentation and Pixel Grouping

Kumar

Koliwad²,

Dwarakish

2008

2008 Sixth Indian Conference on Computer Vision, Graphics &Amp; Image Processing

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A mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. This is an efficient means of early detection of breast cancer. High resolution is a common characteristic of such images. Archiving and retaining these data for at least three years is expensive, difficult and requires sophisticated data compression techniques. In this paper an efficient method is proposed for lossless compression of mammography images. After performing de-correlation of the image using two efficient predictors, the residue image is divided into 4x4 blocks. The blocks with all-zero pixels are identified using one bit code. Later, Second order of pixel grouping is employed to the remaining blocks to increase the coding efficiency. Such blocks are coded using Base offset method. Special techniques are used to save the header information.The method is tested using 25 mammograms from the MIAS database, each having a resolution of 1024x1024 pixels with 8 bits/pixel. Experimental results indicate better compression ratio when compared to JPEG 2000, JPEG-LS, PNG and JBIG.

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Sub-block interchange for lossless image compression

Cited by 5 publications

References 2 publications

An Efficient Low Complexity Lossless Coding Algorithm for Medical Images

An Efficient Low Complexity Lossless Coding Algorithm for Medical Images

Applications of moving windows technique to autonomous vehicle navigation

Lossless Compression of Digital Mammography Using Fixed Block Segmentation and Pixel Grouping

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