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Contour-based Multidirectional Prediction for Intra Coding

基于轮廓的帧内编码多向预测

基本信息

批准号：
397975900
负责人：
Professor Dr.-Ing. Jörn Ostermann
金额：
--
依托单位：
Institut für Informationsverarbeitung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2018
资助国家：
德国
起止时间：
2017-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/397975900?language=en
关键词：
Contour based Multidirectional Prediction Intra

项目摘要

Intra coding is an essential part of all modern image and video codecs. It is used at the beginning of a transmission, for random access into ongoing transmissions, for error resilience, for changing the data rate in video on demand applications as well as for coding of new content in video sequences. Different to inter coding, intra coding does not use any previously transmitted information for improving the coding efficiency. Therefore, the data rate for intra coding is 10-fold to 100-fold compared to inter coding for the same picture quality. This is the reason for conducting further research in the area of spatial prediction for intra coding.Known predictions methods for intra coding do not perform well in case of a signal rich in contours. This is especially true in case of several or non-linear contours. Therefore methods for the prediction of signals containing several contours were developed. Based on the literature and our own work it is known that it is useful to have separate methods for the extrapolation of contours and for the prediction of pixel values. None of the known methods provides a model for contours that can describe linear and non-linear contours as well as contours where the change of direction is predictable. However, a generic model is desirable for proper prediction of these complex contours in order to minimize the data rate. Known methods for the prediction of pixel values are not sufficiently precise, too compute intensive or unsuitable as a prediction signal for future image frames.The goal of this project is the development of more efficient intra coding methods for image coding as well as video coding by combining traditional video coding methods with contour extrapolation and machine learning methods for pixel value prediction. We will model contours in previously coded neighboring blocks using a Gauss process. The model will be appropriate for any contour, be it linear, non-linear or direction changing. Using the Gauss process, we will extrapolate the contours into the areas of the image to be coded next. Using the previously coded image signal and the extrapolated contours as a reference, we will use a CNN to predict the pixel values. The integration of these methods into image and video codecs will show that the coding efficiency of image and video coding can be improved.

帧内编码是所有现代图像和视频编解码器的重要组成部分。它在传输开始时使用，用于随机接入正在进行的传输，用于错误恢复，用于改变视频点播应用中的数据速率以及用于编码视频序列中的新内容。与帧间编码不同，帧内编码不使用任何先前传输的信息来提高编码效率。因此，对于相同的图像质量，帧内编码的数据速率是帧间编码的10倍到100倍。这是在用于帧内编码的空间预测领域中进行进一步研究的原因，用于帧内编码的已知预测方法在富含轮廓的信号的情况下不能很好地执行。在多个或非线性轮廓的情况下尤其如此。因此，开发了用于预测包含多个轮廓的信号的方法。基于文献和我们自己的工作，已知具有用于轮廓的外推和用于像素值的预测的单独方法是有用的。已知的方法中没有一种提供用于轮廓的模型，该模型可以描述线性和非线性轮廓以及方向变化是可预测的轮廓。然而，为了最小化数据速率，需要通用模型来正确预测这些复杂轮廓。已知的像素值预测方法不够精确，计算量太大或不适合作为未来图像帧的预测信号。该项目的目标是通过将传统的视频编码方法与轮廓外推和机器学习方法相结合，开发更有效的图像编码和视频编码方法用于像素值预测。我们将使用高斯过程对先前编码的相邻块中的轮廓进行建模。该模型将适用于任何轮廓，无论是线性，非线性或方向变化。使用高斯过程，我们将把轮廓外推到接下来要编码的图像区域。使用先前编码的图像信号和外推轮廓作为参考，我们将使用CNN来预测像素值。将这些方法集成到图像和视频编解码器中将表明可以提高图像和视频编码的编码效率。