cencro -- Speedup of Video Quality Calculation using Center Cropping.

Today's video streaming providers, e.g. Youtube, Netflix or Amazon Prime, are able to deliver high resolution and high-quality content to end users. To optimize video quality and to reduce transmission bandwidth, new encoders and smarter encoding schemes are required. Encoding optimization forms an important part of this effort in reducing bandwidth and results in saving considerable amount of bitrate. For such optimization, accurate and computationally fast video quality models are required. Netflix's VMAF is an example of such an accurate video quality model. However, it is a full-reference (FR) metric, and the calculation of such metrics tend to be slower in comparison to other metrics, due to the enormous amount of data that needs to be processed, especially for high resolutions of 4k and beyond. We introduce an approach to speed up video quality metric calculations in general. We use VMAF as an example with a video database up to 4K resolution videos, to show that our approach works well. Our main idea is that we reduce each frame of the reference and distorted video based on a center crop of the frame, assuming that most important visual information are presented in the middle of most typical videos. In total we analyze 18 different crop settings and compare our results with uncropped VMAF values and subjective scores. We show that this approach -- named cencro -- is able to save up to 95% computation time, with just an overall error of 4% considering a 360p center crop. Furthermore, we checked other full-reference metrics, and show that cencro performs similar good than for VMAF. As a last evaluation, we apply our approach to full-hd gaming videos, also in this scenario cencro can be successfully applied. The idea behind cencro is not restricted to full-reference models and can also be applied to other type of video quality models or datasets, or even for higher resolution videos such as 8K.