  Rendering high-resolution (HR) graphics brings substantial computational
costs. Efficient graphics super-resolution (SR) methods may achieve HR
rendering with small computing resources and have attracted extensive research
interests in industry and research communities. We present a new method for
real-time SR for computer graphics, namely Super-Resolution by Predicting
Offsets (SRPO). Our algorithm divides the image into two parts for processing,
i.e., sharp edges and flatter areas. For edges, different from the previous SR
methods that take the anti-aliased images as inputs, our proposed SRPO takes
advantage of the characteristics of rasterized images to conduct SR on the
rasterized images. To complement the residual between HR and low-resolution
(LR) rasterized images, we train an ultra-efficient network to predict the
offset maps to move the appropriate surrounding pixels to the new positions.
For flat areas, we found simple interpolation methods can already generate
reasonable output. We finally use a guided fusion operation to integrate the
sharp edges generated by the network and flat areas by the interpolation method
to get the final SR image. The proposed network only contains 8,434 parameters
and can be accelerated by network quantization. Extensive experiments show that
the proposed SRPO can achieve superior visual effects at a smaller
computational cost than the existing state-of-the-art methods.
