In-loop filtering (ILF) is a key technology for removing the artifacts in image/video coding standards. Recently, neural network-based in-loop filtering methods achieve remarkable coding gains beyond the capability of advanced video coding standards, which becomes a powerful coding tool candidate for future video coding standards. However, the utilization of deep neural networks brings heavy time and computational complexity, and high demands of high-performance hardware, which is challenging to apply to the general uses of coding scene. To address this limitation, inspired by explorations in image restoration, we propose an efficient and practical in-loop filtering scheme by adopting the Look-up Table (LUT). We train the DNN of in-loop filtering within a fixed filtering reference range, and cache the output values of the DNN into a LUT via traversing all possible inputs. At testing time in the coding process, the filtered pixel is generated by locating input pixels (to-be-filtered pixel with reference pixels) and interpolating cached filtered pixel values. To further enable the large filtering reference range with the limited storage cost of LUT, we introduce the enhanced indexing mechanism in the filtering process, and clipping/finetuning mechanism in the training. The proposed method is implemented into the Versatile Video Coding (VVC) reference software, VTM-11.0. Experimental results show that the ultrafast, very fast, and fast mode of the proposed method achieves on average 0.13%/0.34%/0.51%, and 0.10%/0.27%/0.39% BD-rate reduction, under the all intra (AI) and random access (RA) configurations. Especially, our method has friendly time and computational complexity, only 101%/102%-104%/108% time increase with 0.13-0.93 kMACs/pixel, and only 164-1148 KB storage cost for a single model. Our solution may shed light on the journey of practical neural network-based coding tool evolution.

翻译：环路滤波是消除图像/视频编码标准中伪影的关键技术。近年来，基于神经网络的环路滤波方法实现了超越先进视频编码标准能力的显著编码增益，成为未来视频编码标准的有力候选工具。然而，深度神经网络的使用带来了巨大的时间和计算复杂度，以及对高性能硬件的高要求，这使其难以广泛应用于编码场景。为解决这一限制，受图像复原领域探索的启发，我们提出了一种采用查找表的高效实用环路滤波方案。我们在固定滤波参考范围内训练环路滤波的深度神经网络，并通过遍历所有可能输入将DNN输出值缓存至查找表中。在编码过程的测试阶段，通过定位输入像素（待滤波像素及其参考像素）并插值缓存的滤波像素值来生成滤波后像素。为在有限存储成本下实现大范围滤波参考，我们在滤波过程中引入了增强索引机制，并在训练中采用了截断/微调机制。所提方法已集成至通用视频编码参考软件VTM-11.0。实验结果表明，该方法在超快速、极快速和快速模式下，在全帧内和随机接入配置下分别平均实现了0.13%/0.34%/0.51%和0.10%/0.27%/0.39%的BD-rate压缩增益。特别值得注意的是，本方法具有友好的时间和计算复杂度：时间开销仅增加101%/102%-104%/108%，计算量为0.13-0.93 kMACs/像素，单个模型存储成本仅为164-1148 KB。我们的解决方案可为基于神经网络的实用编码工具发展提供新的思路。