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基于自适应子带分解和内容模型的图像编码

Adaptive Subband Decomposition and Context-Modeling Based Image Coding

  • 摘要: 在SPIHT算法基础上引入了两种算法--自适应子带分解算法和基于内容模型的算术编码算法,以改进压缩性能.自适应子带分解算法改变了小波变换后系数的统计分布,在低码率时峰值信噪比(PSNR)得到一定的改善;基于内容模型的算术编码算法进一步开发了子带间以及相邻像素间的相关性,增强了压缩性能.实验结果表明:文中算法和SPIHT算法比较,改进了压缩性能,同时保留了零树算法的渐进传输等优点.

     

    Abstract: This paper proposes the adaptive subband decomposition algorithm and context-based entropy arithmetic coding. The adaptive subband decomposition algorithm changes the statistics of transformed coefficients and improves the coding performance in Peak Signal-to-Noise Ratio (PSNR) in low bit rate. In addition, the context model exploits the dependence between subbands and between adjacent pixels and elevates the compression ratio. Experimental results show that the proposed algorithm has the better compression performance than SPIHT and at the time preserves the property of embedded zero tree coding.

     

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