2,473 research outputs found
Video Frame Interpolation via Adaptive Separable Convolution
Standard video frame interpolation methods first estimate optical flow
between input frames and then synthesize an intermediate frame guided by
motion. Recent approaches merge these two steps into a single convolution
process by convolving input frames with spatially adaptive kernels that account
for motion and re-sampling simultaneously. These methods require large kernels
to handle large motion, which limits the number of pixels whose kernels can be
estimated at once due to the large memory demand. To address this problem, this
paper formulates frame interpolation as local separable convolution over input
frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D
kernels require significantly fewer parameters to be estimated. Our method
develops a deep fully convolutional neural network that takes two input frames
and estimates pairs of 1D kernels for all pixels simultaneously. Since our
method is able to estimate kernels and synthesizes the whole video frame at
once, it allows for the incorporation of perceptual loss to train the neural
network to produce visually pleasing frames. This deep neural network is
trained end-to-end using widely available video data without any human
annotation. Both qualitative and quantitative experiments show that our method
provides a practical solution to high-quality video frame interpolation.Comment: ICCV 2017, http://graphics.cs.pdx.edu/project/sepconv
A Matlab-Based Tool for Video Quality Evaluation without Reference
This paper deals with the design of a Matlab based tool for measuring video quality with no use of a reference sequence. The main goals are described and the tool and its features are shown. The paper begins with a description of the existing pixel-based no-reference quality metrics. Then, a novel algorithm for simple PSNR estimation of H.264/AVC coded videos is presented as an alternative. The algorithm was designed and tested using publicly available video database of H.264/AVC coded videos. Cross-validation was used to confirm the consistency of results
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