基于整数小波变换的贝尔图像无损压缩研究
首发时间:2009-09-16
摘要:本文提出了一种基于整数小波变换的贝尔图像无损压缩算法。算法采用bior2.4双正交小波,具有较高的正则性,利于高频分量提取,能有效地将贝尔图像高频特性通过子带系数体现出来,同时对小波系数应用基于上下文Golomb-Rice编码方法,使无损编码效率得到提高。实验表明:与JPEG2000和JPEG-LS相比,本文无损压缩算法以较低的复杂度获得了较高的压缩比。
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RESEARCH ON BAYER IMAGE COMPRESSION BASED ON INTEGER WAVELET TRANSFORM
Abstract:In the paper, we propose five-level Mallat wavelet packet decomposition of Bayer image that is ideally suited to the task of decorrelating mosaic data. Since the lifting scheme of the integer wavelet essentially performs linear prediction on pixels, the wavelet coefficients in the high-frequency subband can be viewed as prediction errors. After low-frequency coefficients prediction, the prediction errors are treated the same as those coefficients in the high-frequency subband. We practice a simple context-based Golomb-Rice coding scheme to compress integer wavelet coefficients of every subband in accord with the raster scan order. Lossless compression ratio can achieve better performance compared to JPEG-LS’s and JPEG2000’s.
Keywords: Bayer image lossless compression wavelet transform
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