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Analysis of Prediction Algorithms for Residual Compression in a Lossy to Lossless Scalable Video Coding System Based on HEVC

机译:基于HEVC的有损至无损可伸缩视频编码系统中残余压缩的预测算法分析

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Lossless image and video compression is required in many professional applications. However, lossless coding results in a high data rate, which leads to a long wait for the user when the channel capacity is limited. To overcome this problem, scalable lossless coding is an elegant solution. It provides a fast accessible preview by a lossy compressed base layer, which can be refined to a lossless output when the enhancement layer is received. Therefore, this paper presents a lossy to lossless scalable coding system where the enhancement layer is coded by means of intra prediction and entropy coding. Several algorithms are evaluated for the prediction step in this paper. It turned out that Sample-based Weighted Prediction is a reasonable choice for usual consumer video sequences and the Median Edge Detection algorithm is better suited for medical content from computed tomography. For both types of sequences the efficiency may be further improved by the much more complex Edge-Directed Prediction algorithm. In the best case, in total only about 2.7% additional data rate has to be invested for scalable coding compared to single-layer JPEG-LS compression for usual consumer video sequences. For the case of the medical sequences scalable coding is even more efficient than JPEG-LS compression for certain values of QP.
机译:在许多专业应用中需要无损图像和视频压缩。然而,无损编码导致高数据速率,这在信道容量受限时导致用户的长时间等待。为了克服这个问题,可伸缩的无损编码是一种很好的解决方案。它通过有损压缩基础层提供了快速可访问的预览,可以在接收增强层时将其完善为无损输出。因此,本文提出了一种有损到无损的可伸缩编码系统,其中增强层是通过帧内预测和熵编码来编码的。本文针对预测步骤评估了几种算法。事实证明,基于样本的加权预测是通常的消费者视频序列的合理选择,并且中值边缘检测算法更适合于计算机断层扫描的医学内容。对于两种类型的序列,效率都可以通过更加复杂的边沿预测算法来进一步提高。在最佳情况下,与普通消费者视频序列的单层JPEG-LS压缩相比,可扩展编码总共只需要增加大约2.7%的额外数据速率。对于医学序列而言,对于某些QP值,可伸缩编码甚至比JPEG-LS压缩更有效。

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