Zhang Yong, Adjeroh Donald A
Center for Biotechnology and Informatics, Department of Radiology, The Methodist Research Institute, Houston, TX 77030-2707, USA.
IEEE Trans Image Process. 2008 Jun;17(6):924-35. doi: 10.1109/TIP.2008.920772.
Context-based modeling is an important step in high-performance lossless data compression. To effectively define and utilize contexts for natural images is, however, a difficult problem. This is primarily due to the huge number of contexts available in natural images, which typically results in higher modeling costs, leading to reduced compression efficiency. Motivated by the prediction by partial matching context model that has been very successful in text compression, we present prediction by partial approximate matching (PPAM), a method for compression and context modeling for images. Unlike the PPM modeling method that uses exact contexts, PPAM introduces the notion of approximate contexts. Thus, PPAM models the probability of the encoding symbol based on its previous contexts, whereby context occurrences are considered in an approximate manner. The proposed method has competitive compression performance when compared with other popular lossless image compression algorithms. It shows a particularly superior performance when compressing images that have common features, such as biomedical images.
基于上下文的建模是高性能无损数据压缩中的重要一步。然而，要有效地为自然图像定义和利用上下文是一个难题。这主要是由于自然图像中存在大量的上下文，这通常会导致更高的建模成本，进而降低压缩效率。受在文本压缩中非常成功的部分匹配上下文模型的预测启发，我们提出了部分近似匹配预测（PPAM），一种用于图像压缩和上下文建模的方法。与使用精确上下文的PPM建模方法不同，PPAM引入了近似上下文的概念。因此，PPAM基于编码符号的先前上下文对其概率进行建模，从而以近似方式考虑上下文出现的情况。与其他流行的无损图像压缩算法相比，该方法具有有竞争力的压缩性能。在压缩具有共同特征的图像（如生物医学图像）时，它表现出特别优越的性能。
