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In this paper, we propose a weakly connected dense attention neural network for compression artifact removal, called WCDANN. WCDANN is a convolutional neural network (CNN)-based post-processing filter to enhance the quality of versatile video coding (VVC)-decoded videos without requiring any codec changes. WCDANN consists of several weakly connected dense attention blocks (WCDABs) based on residual learning, which takes the compressed video after codecs as the input. We use depthwise separable convolution for WCDANN as the basic convolution unit to generate a lightweight model. Moreover, we introduce attention mechanisms into the proposed filter to capture important features. Experimental results show that WCDANN achieves good performance in Bj&#x00F8;ntegaard Delta Bit Rate (BD-BR). Compared with VTM-11.0-NNVC anchor, WCDANN achieves average 2.81%, 4.12% and 3.81% BD-rate reductions for Y channel on A1, A2, B, C, D and E classes in RA, AI and LDP configurations, respectively.

WCDANN: A Lightweight CNN Post-Processing Filter for VVC-Based Video Compression