论文标题
OL-DN：基于在线学习的HEVC内部框架质量增强的双域网络
OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement
论文作者
论文摘要
基于卷积神经网络（CNN）方法提供了有效的解决方案，以增强压缩图像和视频的质量。但是，这些方法忽略了使用原始数据来增强质量的方法。在本文中，我们通过提出一种基于在线学习的方法来采用HEVC内编码图像的质量增强质量增强的原始数据。当需要增强质量时，我们在线培训我们在编码器一侧提出的模型，然后使用参数来更新解码器端的模型。此方法不仅可以改善模型性能，而且还可以使一个模型可在多个编码方案中采用。此外，离散余弦变换（DCT）系数中的量化误差是各种HEVC压缩伪像的根本原因。因此，我们结合了频域先验以协助图像重建。我们设计了一个基于DCT的卷积层，以产生适合CNN学习的DCT系数。实验结果表明，与最先进的方法相比，我们提出的基于在线学习的双域网络（OL-DN）取得了出色的性能。
Convolution neural network (CNN) based methods offer effective solutions for enhancing the quality of compressed image and video. However, these methods ignore using the raw data to enhance the quality. In this paper, we adopt the raw data in the quality enhancement for the HEVC intra-coded image by proposing an online learning-based method. When quality enhancement is demanded, we online train our proposed model at encoder side and then use the parameters to update the model of decoder side. This method not only improves model performance, but also makes one model adoptable to multiple coding scenarios. Besides, quantization error in discrete cosine transform (DCT) coefficients is the root cause of various HEVC compression artifacts. Thus, we combine frequency domain priors to assist image reconstruction. We design a DCT based convolution layer, to produce DCT coefficients that are suitable for CNN learning. Experimental results show that our proposed online learning based dual-domain network (OL-DN) has achieved superior performance, compared with the state-of-the-art methods.
