1 research outputs found
A Group Variational Transformation Neural Network for Fractional Interpolation of Video Coding
Motion compensation is an important technology in video coding to remove the
temporal redundancy between coded video frames. In motion compensation,
fractional interpolation is used to obtain more reference blocks at sub-pixel
level. Existing video coding standards commonly use fixed interpolation filters
for fractional interpolation, which are not efficient enough to handle diverse
video signals well. In this paper, we design a group variational transformation
convolutional neural network (GVTCNN) to improve the fractional interpolation
performance of the luma component in motion compensation. GVTCNN infers samples
at different sub-pixel positions from the input integer-position sample. It
first extracts a shared feature map from the integer-position sample to infer
various sub-pixel position samples. Then a group variational transformation
technique is used to transform a group of copied shared feature maps to samples
at different sub-pixel positions. Experimental results have identified the
interpolation efficiency of our GVTCNN. Compared with the interpolation method
of High Efficiency Video Coding, our method achieves 1.9% bit saving on average
and up to 5.6% bit saving under low-delay P configuration.Comment: DCC 201