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Resolution scalable and random access decodable image coding with low time complexity.

机译：具有低时间复杂度的分辨率可扩展和随机访问可解码图像编码。

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Modern wavelet-based image compression methods provide not only higher compression performance, but also the capability to support various features, such as quality (SNR) scalability, resolution scalability, and region-of-interest encoding and decoding. Quality scalability is commonly achieved via bit-plane coding, which also helps to improve compression, since neighboring bits provide convenient and powerful contexts for entropy coding. However, on many important applications (e.g. digital camera), the images always need to have a pre-defined high quality, and any extra effort required for quality scalability is wasted. Furthermore, for compressing a very large size image source, a low time complexity is often the most desirable characteristic of an image coding algorithm.; In this thesis, a resolution scalable and random accessible image coding algorithm, PROGRES (Progressive Resolution Decompression), is designed based on predictive dynamic range coding of wavelet coefficients and without bit-plane coding. Avoiding bit-plane coding leads to considerable speed improvement without compromising coding efficiency. The algorithm is designed and implemented for both 2D and 3D image sources. Experiments show that our suggested coding model lessens the computational burden of bit-plane based image coding, both in encoding and decoding time.; The PROGRES algorithm combined with the presented fast random access decoding method having O(log2 n) block seek time is suitable for browsing a very large image bitstream. It can seek the requested part in the code-stream very quickly, and then decode them upto desired resolution at high speed.; In related work, we introduce the concept of higher order zerotrees in modern wavelet-based coders and quantify their relative coding power. By analyzing two famous zerotree-based image coders, EZW and SPIHT, we are able to explain the superior coding efficiency of SPIHT through its ability to code higher order zerotrees than EZW. We are also able to calculate the bit savings of SPIHT compared to EZW within this framework.
机译：基于现代小波的图像压缩方法不仅提供更高的压缩性能，而且还能够支持各种功能，例如质量（SNR）可伸缩性，分辨率可伸缩性以及关注区域的编码和解码。质量可伸缩性通常是通过位平面编码来实现的，这也有助于改善压缩，因为相邻位为熵编码提供了方便而强大的上下文。但是，在许多重要的应用程序（例如数码相机）上，图像始终需要具有预定义的高质量，并且浪费了质量可伸缩性所需的任何额外工作。此外，对于压缩非常大尺寸的图像源，低时间复杂度通常是图像编码算法最期望的特性。本文基于小波系数的动态预测范围编码，无需位平面编码，设计了一种分辨率可扩展，可随机访问的图像编码算法PROGRES（渐进分辨率解压缩）。避免位平面编码可在不影响编码效率的情况下显着提高速度。该算法是针对2D和3D图像源设计和实现的。实验表明，我们提出的编码模型在编码和解码时间上都减轻了基于位平面的图像编码的计算负担。结合提出的具有O（log2 n）块寻道时间的快速随机访问解码方法的PROGRES算法适用于浏览非常大的图像比特流。它可以非常快地在码流中寻找所需的部分，然后将它们高速解码到所需的分辨率。在相关工作中，我们介绍了现代小波编码器中高阶零树的概念，并量化了它们的相对编码能力。通过分析两个著名的基于零树的图像编码器EZW和SPIHT，我们能够通过SPIHT编码比EZW高阶零树的能力来解释SPIHT优越的编码效率。在此框架内，与EZW相比，我们还能够计算SPIHT的位节省。

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