Fast Image Quality Enhancement for HEVC by Postfiltering via Shallow Neural Networks

抄録

The study proposes a lightweight adaptive postfilter based on neural networks for use inH.265 High Efficiency Video Coding (HEVC). The proposed filter is adaptive because it uses a different set of parameters based on encoding settings and most significantly on the quantization parameter. With the aforementioned information, the filter most efficiently improves each block. We trained the filter for 4 different QP values and we demonstrate that the use of the filter leads to a decrease in bitrate of over 4% in a few cases and a decrease in bitrate of 1.5% on average for both All Intra and Random Access modes.In contrast to a few filters that use several passes and require specific ordering, the proposed filter changes each pixel at most once and the input uses only initial values, thereby allowing perfect parallelization.Furthermore, the use of only one convolutional layer and eight feature layers maintains the computing cost and memory footprint to the minimum possible extent, and this makes real-time processing possible even on embedded hardware.