论文标题
通过神经网络重新访问VVC的样品自适应偏移器
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks
论文作者
论文摘要
在HEVC中引入了样品自适应偏移量（SAO）滤波器，以减少重建图片中的一般编码和束带伪像，以补充De块滤波器（DBF），该滤波器（DBF）专门降低了块边界处的伪像。与HEVC相比，新型视频压缩标准视频编码（VVC）以相同的重建质量将BD率降低了约36％。它实现了DBF顶部和SAO过滤器上的额外的新环内自适应环滤波器（ALF），与HEVC相比，后者保持不变。但是，SAO在VVC中的相对性能已显着降低。在本文中，建议使用神经网络（NN）重新审视SAO过滤器。保留了SAO的一般原则，但是SAO的A-Priori分类被一组神经网络所取代，这些神经网络确定应纠正哪些重建样品以及哪些比例。与原始的SAO类似，在编码器侧确定了一些参数，并编码每个CTU。拟议的SAO的平均BD率增益将VVC的随机访问至少提高2.3％，而与其他基于NN的方法相比，总体复杂性保持相对较小。
The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking Filter (DBF) which reduces artifacts at block boundaries specifically. The new video compression standard Versatile Video Coding (VVC) reduces the BD-rate by about 36% at the same reconstruction quality compared to HEVC. It implements an additional new in-loop Adaptive Loop Filter (ALF) on top of the DBF and the SAO filter, the latter remaining unchanged compared to HEVC. However, the relative performance of SAO in VVC has been lowered significantly. In this paper, it is proposed to revisit the SAO filter using Neural Networks (NN). The general principles of the SAO are kept, but the a-priori classification of SAO is replaced with a set of neural networks that determine which reconstructed samples should be corrected and in which proportion. Similarly to the original SAO, some parameters are determined at the encoder side and encoded per CTU. The average BD-rate gain of the proposed SAO improves VVC by at least 2.3% in Random Access while the overall complexity is kept relatively small compared to other NN-based methods.
