Abstract. 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-theart video coding standard. We redesign a Variable-filter-size Residuelearning 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.
For the quality enhancement of video compression, VRCNN REF was proposed as a variable-filter-size residuelearning convolutional neural networks for the post processing of HEVC intra coding.
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
ArXiv
ArXiv
Computer Science