OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement
This work addresses video compression quality enhancement for HEVC users, but it is incremental as it builds on existing CNN methods with online learning and dual-domain integration.
The paper tackled the problem of enhancing the quality of HEVC intra-coded images by proposing an online learning-based dual-domain network that uses raw data and frequency domain priors, achieving superior performance compared to state-of-the-art methods.
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.