基于分割的BTC-VQ图像压缩技术

Qosai Kanafani, Azeddine Beghdadi, C. Fookes
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引用次数: 17

摘要

本文提出了一种基于EM算法的图像分割,并结合BTC(块截断编码)和VQ(矢量量化)的图像压缩新方法。主要思想是将图像分解为均匀和非均匀块,然后使用BTC或VQ压缩它们。这种块分类是使用基于EM(期望最大化)算法的图像分割实现的。EM算法的使用使分割结果具有良好的鲁棒性和良好的边界。然后使用分割后的图像来指定是否使用BTC或VQ来编码块,通过评估它是否包含来自均匀或非均匀区域的所有像素。BTC由于其两级量化器,为包含大量信息或边缘明显的块的编码提供了一种简单有效的方法。然而,其最低可达到的比特率是有限的,并且经常在均匀区域引入阻塞效应。另一方面,由于多电平量化器,VQ更有效,从而产生更好的压缩比。然而,它没有保留任何关于边缘的空间信息,导致楼梯框效果。以前将两种技术结合成混合算法的尝试只使用简单的度量,如图像方差。医学图像的结果表明,当单独使用时,这种方法比传统的BTC或VQ编码产生显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation-based image compression using BTC-VQ technique
This paper proposes a new approach to image compression based on image segmentation using the EM algorithm and combined with BTC (block truncation coding) and VQ (vector quantization). The main idea is to decompose the image into homogeneous and nonhomogeneous blocks and then compress them using BTC or VQ. This block classification is achieved using an image segmentation based on the EM (expectation-maximization) algorithm. The use of the EM algorithm results in a good robust segmentation with well behaved boundaries. The segmented image is then used to specify whether BTC or VQ is used to encode a block by assessing if it contains all pixels from a homogeneous or nonhomogeneous region. BTC provides a simple and effective method for coding blocks which contain a lot of information or distinct edges due to its two-level quantizer. However, its lowest attainable bit rate is limited and it often introduces blocking effect in homogeneous regions. VQ on the other hand is more efficient due to a multilevel quantizer and thus results in better compression ratios. However, it does not retain any spatial information about the edges, resulting in stair casing effects. Previous attempts to combine both techniques into a hybrid algorithm only make use of simple measures such as image variance. Results for medical images show that this approach yields significant improvements over traditional BTC or VQ coding when used alone.
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