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Convolution neural network (CNN) based methods offer effective solutions forenhancing the quality of compressed image and video. However, these methodsignore using the raw data to enhance the quality. In this paper, we adopt theraw data in the quality enhancement for the HEVC intra-coded image by proposingan online learning-based method. When quality enhancement is demanded, weonline train our proposed model at encoder side and then use the parameters toupdate the model of decoder side. This method not only improves modelperformance, but also makes one model adoptable to multiple coding scenarios.Besides, quantization error in discrete cosine transform (DCT) coefficients isthe root cause of various HEVC compression artifacts. Thus, we combinefrequency domain priors to assist image reconstruction. We design a DCT basedconvolution layer, to produce DCT coefficients that are suitable for CNNlearning. Experimental results show that our proposed online learning baseddual-domain network (OL-DN) has achieved superior performance, compared withthe state-of-the-art methods.

OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement