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High-performance compression of visual information-a tutorial review. I. Still pictures

机译：视觉信息的高性能压缩-教程复习。一，静态图片

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摘要

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful pairs. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must he defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state-of-the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated.
机译：在现代通信系统中，数字图像已成为重要的信息来源。原始格式的数字图像需要大量的存储空间。在过去的二十年中，许多研究工作致力于图像压缩问题。必须区分两个不同的压缩类别：无损和有损。如果在编码图像中没有引入失真，则可以实现无损压缩。需要此类压缩的应用包括医学成像和卫星摄影。对于诸如视频电话或多媒体应用之类的应用，通常容忍一些信息丢失以换取高压缩比。在这篇由两部分组成的论文中，概述了图像编码方案的主要组成部分。第一部分介绍了静止图像编码，第二部分介绍了运动图像序列。在第一部分中，静止图像编码方案已被分类为预测，块变换和多分辨率方法。预测方法适用于无损和低压缩应用。基于变换的编码方案为有损压缩实现了更高的压缩率，但在高压缩率下会出现阻塞伪像的问题。多分辨率方法也适用于有损压缩和无损压缩。在有损的高压缩比下，重建图像中可见的典型伪像是振铃效果。多媒体环境中的新应用推动了对图像编码方案的新功能的需求。为此，第二代编码技术将图像分成语义上有意义的对。因此，这些方法的一部分已经适应于对任意形状的区域进行工作。为了增加另一功能，例如信息的渐进传输，必须定义特定的量化算法。压缩方案的最后一步是通过码字分配实现的。最后，给出了编码结果，这些结果比较了有损和无损压缩的最新技术。强调并讨论了每种技术的不同工件。而且，示出了渐进式传输的可能性。

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