  In this paper, we propose a partition-masked Convolution Neural Network (CNN)
to achieve compressed-video enhancement for the state-of-the-art coding
standard, High Efficiency Video Coding (HECV). More precisely, our method
utilizes the partition information produced by the encoder to guide the quality
enhancement process. In contrast to existing CNN-based approaches, which only
take the decoded frame as the input to the CNN, the proposed approach considers
the coding unit (CU) size information and combines it with the distorted
decoded frame such that the degradation introduced by HEVC is reduced more
efficiently. Experimental results show that our approach leads to over 9.76%
BD-rate saving on benchmark sequences, which achieves the state-of-the-art
performance.
