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 convo-lutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG com-pressed 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.
CITATION STYLE
Claypool, M., Eg, R., & Raaen, K. (2017). Modeling User Performance for Moving Target. MultiMedia Modeling, 1, 226–237. https://doi.org/10.1007/978-3-319-51811-4
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