Training and Predicting Visual Error for Real-Time Applications
João Libório Cardoso Bernhard Kerbl Lei Yang Yury Uralsky Michael Wimmer
Visual error metrics play a fundamental role in the quantification of
perceived image similarity. Most recently, use cases for them in real-time
applications have emerged, such as content-adaptive shading and shading reuse
to increase performance and improve efficiency. A wide range of different
metrics has been established, with the most sophisticated being capable of
capturing the perceptual characteristics of the human visual system. However,
their complexity, computational expense, and reliance on reference images to
compare against prevent their generalized use in real-time, restricting such
applications to using only the simplest available metrics. In this work, we
explore the abilities of convolutional neural networks to predict a variety of
visual metrics without requiring either reference or rendered images.
Specifically, we train and deploy a neural network to estimate the visual error
resulting from reusing shading or using reduced shading rates. The resulting
models account for 70%-90% of the variance while achieving up to an order of
magnitude faster computation times. Our solution combines image-space
information that is readily available in most state-of-the-art deferred shading
pipelines with reprojection from previous frames to enable an adequate estimate
of visual errors, even in previously unseen regions. We describe a suitable
convolutional network architecture and considerations for data preparation for
training. We demonstrate the capability of our network to predict complex error
metrics at interactive rates in a real-time application that implements
content-adaptive shading in a deferred pipeline. Depending on the portion of
unseen image regions, our approach can achieve up to $2\times$ performance
compared to state-of-the-art methods.