A Convolutional Neural Network Approach for Post Processing in HEVC Intra Coding
Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit rates. To reduce these artifacts, post processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post processing, mostly for JPEG compressed images. In this paper, we present a CNN based post processing algorithm for High Efficiency Video Coding (HEVC) the state of the art video coding standard. We redesign a Variable filter size Residue learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post processing leads to on average 4.6% bit rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit rate reduction, lower memory cost, and multiplied computational speedup.
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