  The layers of convolutional neural networks (CNNs) can be used to alter the
resolution of their inputs, but the scaling factors are limited to integer
values. However, in many image and video processing applications, the ability
to resize by a fractional factor would be advantageous. One example is
conversion between resolutions standardized for video compression, such as from
1080p to 720p. To solve this problem, we propose an alternative building block,
formulated as a conventional convolutional layer followed by a differentiable
resizer. More concretely, the convolutional layer preserves the resolution of
the input, while the resizing operation is fully handled by the resizer. In
this way, any CNN architecture can be adapted for non-integer resizing. As an
application, we replace the resizing convolutional layer of a modern deep
downsampling model by the proposed building block, and apply it to an adaptive
bitrate video streaming scenario. Our experimental results show that an
improvement in coding efficiency over the conventional Lanczos algorithm is
attained, in terms of PSNR, SSIM, and VMAF on test videos.
