MC-JBIG2: an improved algorithm for Chinese textual image compression

Medical image compression technology has great significance in the field of medical image engineering and clinical application. The current image compression technology is mainly for gray image, Few people study on medical image color compression algorithm. This paper presents a compression algorithm for color image based on DCT. First, the algorithm change the color medical image of RGB space to SAT space, this transformation ensures the medical image without distortion, and effectively reduce the mean image of each dimension of component. Then, the algorithm make the DCT transform for color image compression in the SAT space. Experimental shows that the improved algorithm in color medical image compression has achieved good results.

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BASED ON RANGE AND DOMAIN FRACTAL IMAGE COMPRESSION OF SATELLITE IMAGERIES IMPROVED ALGORITHM FOR RESEARCH

International Journal of Electronics and Electical Engineering ◽

10.47893/ijeee.2014.1130 ◽

2014 ◽

pp. 71-74

Author(s):

SAEMA ENJELA ◽

A.G. ANANTH

Keyword(s):

Image Compression ◽

Image Coding ◽

Fractal Image Compression ◽

Compression Technique ◽

Fractal Coding ◽

Fractal Compression ◽

Urban Image ◽

Fractal Image ◽

Domain Block ◽

Improved Algorithm

Fractal coding is a novel method to compress images, which was proposed by Barnsley, and implemented by Jacquin. It offers many advantages. Fractal image coding has the advantage of higher compression ratio, but is a lossy compression scheme. The encoding procedure consists of dividing the image into range blocks and domain blocks and then it takes a range block and matches it with the domain block. The image is encoded by partitioning the domain block and using affine transformation to achieve fractal compression. The image is reconstructed using iterative functions and inverse transforms. However, the encoding time of traditional fractal compression technique is too long to achieve real-time image compression, so it cannot be widely used. Based on the theory of fractal image compression; this paper raised an improved algorithm form the aspect of image segmentation. In the present work the fractal coding techniques are applied for the compression of satellite imageries. The Peak Signal to Noise Ratio (PSNR) values are determined for images namely Satellite Rural image and Satellite Urban image. The Matlab simulation results for the reconstructed image shows that PSNR values achievable for Satellite Rural image ~33 and for Satellite urban image ~42.

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Improved SPIHT Algorithm for Texture Image Compression

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.619.311 ◽

2014 ◽

Vol 619 ◽

pp. 311-315

Author(s):

Bao Xia Cui ◽

Yun Ze Wu

Keyword(s):

Image Compression ◽

Signal To Noise Ratio ◽

Low Frequency ◽

Wavelet Coefficients ◽

Texture Image ◽

Original Algorithm ◽

Convolution Process ◽

Lifting Wavelet ◽

Spiht Algorithm ◽

Improved Algorithm

Aiming at the problems of complicated convolution process of traditional wavelet transform and the unsatisfied effect of SPIHT algorithm for texture image compression, an improved SPIHT algorithm for texture image compression is proposed. At first, the texture image is decomposed into N order with the help of the lifting wavelet and the first-order high frequency sub-bands are decomposed into N-1 order by the lifting wavelet, and then the wavelet coefficients are encoded by the improved SPIHT algorithm. The improved SPIHT algorithm improved the process method of the wavelet coefficients in the low-frequency sub-bands and the detection method of some important coefficient in the L collection of the original SPIHT algorithm. Experiments show that the improved algorithm can retain the texture information of texture image more effectively and the quality of reconstructed image and peak signal to noise ratio are better than the original algorithm at the same rate. The effect is better especially at low rate, so the improved algorithm is an efficient compression method for texture image.

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