无损和致损图像压缩的算术编码

C. D. Hardin, S. Zabele
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引用次数: 1

摘要

只提供摘要形式。算术编码已被应用于提供光学、红外和合成孔径雷达图像的自然场景的无损和致损压缩。考虑了几种不同的上下文,包括预测性和非预测性变化,图像依赖和图像独立变化。在无损编码实验中,算术编码算法已被证明比Huffman和Lempel-Ziv-Welch编码算法的可比变体高出约0.5 b/像素。对于由高阶自回归预测器构建的图像相关上下文,算术编码算法提供高达4的压缩比。由低阶自回归预测因子构建的上下文提供的压缩比几乎与具有有利计算交易的高阶预测因子一样大。压缩性能的变化已经被证明反映了图像随机结构中固有的传感器依赖差异。算术编码也被证明是一个有价值的附加的损失诱导压缩技术
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Arithmetic coding for lossless and loss-inducing image compression
Summary form only given. Arithmetic coding has been applied to provide lossless and loss-inducing compression of optical, infrared, and synthetic aperture radar imagery of natural scenes. Several different contexts have been considered, including both predictive and nonpredictive variations, with both image-dependent and image-independent variations. In lossless coding experiments, arithmetic coding algorithms have been shown to outperform comparable variants of both Huffman and Lempel-Ziv-Welch coding algorithms by approximately 0.5 b/pixel. For image-dependent contexts constructed from high-order autoregressive predictors, arithmetic coding algorithms provide compression ratios as high as four. Contexts constructed from lower-order autoregressive predictors provide compression ratios nearly as great as those of the higher-order predictors with favorable computational trades. Compression performance variations have been shown to reflect the inherent sensor-dependent differences in the stochastic structure of the imagery. Arithmetic coding has also been demonstrated to be a valuable addition to loss-inducing compression techniques.<>
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