Losless and lossy compression of screen-content data using machine learning

使用机器学习对屏幕内容数据进行无损和有损压缩

• 批准号: 438221930
• 负责人: Professor Dr.-Ing. André Kaup
• 金额: --
• 依托单位: Lehrstuhl für Multimediakommunikation und Signalverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/438221930?language=en
• 关键词: Losless; lossy; compression; screen; content

This project explores a novel approach to the compression of screen-content data, taking into account different facets of image properties and the specific objective of compression.In contrast to classical photo or broadcast-video data, the content of screen-content images is very diverse with regard to its statistical properties. Often, certain regions within the images are characterized by two typical properties: a limited number of colours and repeating patterns.Studies have shown that conventional compression methods are not able to store screen-content data efficiently even with the use of special tools. A method based on ideal data coding is much more successful. The success of this new method depends on an optimal modelling of the probability distributions of the pixel symbols.For this new compression method, a conceptual approach (prototype) has already been realized, which is however still limited to lossless compression and achieves a high compression efficiency only for image contents with certain properties.The project investigates new methods for modelling the probability distributions of pixel symbols using machine-learning methods. Overall, the project pursues several approaches:• The existing prototype only uses global image information in some processing steps for modelling the probability distributions. Estimation methods that incorporate more prior knowledge, e.g. of more local nature, are expected to yield significant gains in compression. Alternative learning methods are being considered for this purpose. In general, it is about learning with a few examples. For this approach, higher compression ratios can be achieved than with classical methods, which first segment the image into regions of different types and then switch to conventional compression methods if necessary.• The research project will also investigate how a suitable rate-distortion optimization can extend the procedure to a near-lossless or lossy compression. A dedicated image analysis can parameterize the optimization in order to consider perceptual models of human vision. • An extension of the conceptual approach to image sequence compression is possible in principle and is aimed at. The consideration of the temporal component can lead to an improved modelling of the probability distribution depending on the situation and must therefore be researched. In image sequences, changes in signal statistics typically occur due to new content or scene changes. In particular, the research project should investigate how the existing learning procedure can be supplemented with suitable elements for forgetting or relearning.

该项目探索了一种新的方法来压缩屏幕内容数据，考虑到图像属性的不同方面和压缩的特定目标。与经典的照片或广播视频数据相比，屏幕内容图像的内容在统计属性方面非常多样化。通常，图像中的某些区域具有两个典型的特性：有限数量的颜色和重复的图案。研究表明，即使使用特殊工具，传统的压缩方法也无法有效地存储屏幕内容数据。基于理想数据编码的方法是比较成功的。这种新方法的成功取决于像素符号的概率分布的最佳模型。（原型）已经实现，然而，该方法仍然局限于无损压缩，并且仅对具有某些属性的图像内容实现高压缩效率。该项目研究了使用机器学习对像素符号的概率分布建模的新方法。学习方法总的来说，该项目采用了几种方法：· 现有的原型仅在一些处理步骤中使用全局图像信息来对概率分布进行建模。结合更多先验知识（例如，更多局部性质的先验知识）的估计方法预期在压缩中产生显著增益。为此目的，正在考虑其他学习方法。总的来说，这是关于学习几个例子。对于这种方法，可以实现比经典方法更高的压缩比，后者首先将图像分割成不同类型的区域，然后在必要时切换到传统压缩方法。· 该研究项目还将研究如何通过适当的率失真优化将该过程扩展到近无损或有损压缩。专用图像分析可以参数化优化，以便考虑人类视觉的感知模型。· 图像序列压缩的概念方法的扩展在原则上是可能的，并且旨在。对时间部分的考虑可以导致根据情况改进概率分布的模型，因此必须进行研究。在图像序列中，信号统计的变化通常由于新内容或场景变化而发生。特别是，研究项目应调查现有的学习程序如何能够补充适当的遗忘或重新学习的元素。