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A New Fast Algorithm for Sample Adaptive Offset

机译:一种新的样本自适应偏移快速算法

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Sample Adaptive Offset is a new adopted technology by HEVC in recent years, which improves the visual quality of reconstructed videos significantly. However, there are two problems in current SAO technology. The first is that the statistic phase needs to traverse each pixel to collect relevant information. The other problem is that the complexity of SAO is too high for SAO mode decision stage, which needs to be performed on each CTU. To solve these problems, we proposed a fast SAO algorithm in HEVC encoder. We explore the correlation of SAO type among neighboring CTUs, and then utilize this spatial information to reduce the complexity of SAO. Experimental results demonstrate that our proposed method can achieve about 62%, 80% and 75% SAO encoding time saving on average in AI, RA, and LDB test condition compared with HM16.0 respectively. At the same time, the proposed method just causes negligible compression performance loss.
机译:样本自适应偏移是HEVC近年来采用的一项新技术,可显着提高重建视频的视觉质量。然而,当前的SAO技术存在两个问题。首先是统计阶段需要遍历每个像素以收集相关信息。另一个问题是,对于SAO模式决策阶段而言,SAO的复杂度过高,需要在每个CTU上执行。为了解决这些问题,我们提出了一种在HEVC编码器中的快速SAO算法。我们探索了相邻CTU之间SAO类型的相关性,然后利用此空间信息来降低SAO的复杂性。实验结果表明,与HM16.0相比,在AI,RA和LDB测试条件下,我们提出的方法平均可节省约62%,80%和75%的SAO编码时间。同时,所提出的方法仅引起可忽略的压缩性能损失。

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