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基于DPCM的渐进图像压缩和编码格式
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2009-07-08
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提出了一种基于DPCM的从有损到无损的渐进图像压缩和编码格式。算法分为两个
阶段。第一个阶段采用基于马尔可夫模型预测的DPCM算法得到低分辨率的近似图像；第二个阶段对量化误差进行渐进传输，使图像被渐进地完善。它通过对量化误差实施小波变换，根据量化误差的局部频率特性，用简单的规则把小波变换后的系数进行分组，最终使得渐进图像传输成为可能。实验结果表明，由于使用了马尔可夫模型和小波变换，可以更快的得到细节丰富的图像。
关键词:图像压缩；渐进图像传输；DPCM；自适应预测；小波变换
Abstract: A DPCM-based lossy-to-lossless progressive image compression and transmission
scheme is proposed. The method consists of two phases: the first phase makes use of DPCM
algorithm transmitting a low-resolution approximation of the image, in which Markov model is
adopted when predicting; The second phase transmits quantitative errors progressively, therefore the low-resolution image can be then progressively improved. The second phase first transforms the quantitative errors by wavelet transform, then employing a low-complexity classification process to separate transformed quantitative errors into multiple teams according to local frequency feature, so that progressive transmission becomes possible. The experimental results show that the presented method could refine the approximation image as quickly as possible because of the use of Markov model and wavelet transform.
Keywords: Image compression; Progressive image transmission; DPCM; Adaptive
prediction; Wavelet transform
阶段。第一个阶段采用基于马尔可夫模型预测的DPCM算法得到低分辨率的近似图像；第二个阶段对量化误差进行渐进传输，使图像被渐进地完善。它通过对量化误差实施小波变换，根据量化误差的局部频率特性，用简单的规则把小波变换后的系数进行分组，最终使得渐进图像传输成为可能。实验结果表明，由于使用了马尔可夫模型和小波变换，可以更快的得到细节丰富的图像。
关键词:图像压缩；渐进图像传输；DPCM；自适应预测；小波变换
Abstract: A DPCM-based lossy-to-lossless progressive image compression and transmission
scheme is proposed. The method consists of two phases: the first phase makes use of DPCM
algorithm transmitting a low-resolution approximation of the image, in which Markov model is
adopted when predicting; The second phase transmits quantitative errors progressively, therefore the low-resolution image can be then progressively improved. The second phase first transforms the quantitative errors by wavelet transform, then employing a low-complexity classification process to separate transformed quantitative errors into multiple teams according to local frequency feature, so that progressive transmission becomes possible. The experimental results show that the presented method could refine the approximation image as quickly as possible because of the use of Markov model and wavelet transform.
Keywords: Image compression; Progressive image transmission; DPCM; Adaptive
prediction; Wavelet transform
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