基于上下文的有效熵编码用于有损小波图像压缩

C. Chrysafis, Antonio Ortega
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引用次数: 135

摘要

提出了一种基于后向自适应量化/分类技术的自适应图像编码算法。我们使用一个简单的均匀标量量化器来量化图像子带。我们的算法根据相邻的先前量化系数的值将系数划分为若干类中的一类。这些先前量化的系数形成用于表征子带数据的上下文。对于每个上下文类型对应一个不同的概率模型,因此每个子带系数用一个算术编码器压缩,该编码器根据该系数的邻域具有适当的模型。我们展示了上下文选择是如何由率失真标准驱动的,通过选择给定比特率的总失真最小化的方式来选择上下文。此外,每个上下文的概率模型都以非常有效的方式初始化/更新,因此实际上不需要向解码器发送开销信息。我们的结果与最近的技术水平相当,甚至在某些情况下更好,我们的算法比大多数已发布的具有可比性能的算法更简单。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient context-based entropy coding for lossy wavelet image compression
We present an adaptive image coding algorithm based on novel backward-adaptive quantization/classification techniques. We use a simple uniform scalar quantizer to quantize the image subbands. Our algorithm puts the coefficient into one of several classes depending on the values of neighboring previously quantized coefficients. These previously quantized coefficients form contexts which are used to characterize the subband data. To each context type corresponds a different probability model and thus each subband coefficient is compressed with an arithmetic coder having the appropriate model depending on that coefficient's neighborhood. We show how the context selection can be driven by rate-distortion criteria, by choosing the contexts in a way that the total distortion for a given bit rate is minimized. Moreover the probability models for each context are initialized/updated in a very efficient way so that practically no overhead information has to be sent to the decoder. Our results are comparable or in some cases better than the recent state of the art, with our algorithm being simpler than most of the published algorithms of comparable performance.
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