Object Tracking-based Foveated Super-Resolution Convolutional Neural Network for Head Mounted Display (Topic: Image and Video Processing Applications)
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TimeThursday, 6 December 20189am - 6pm
DescriptionWe propose a novel foveated super-resolution convolutional neural network (SRCNN) for HMD using an object tracking algorithm to reduce computation load for rendering high resolution images. We implement the object tracking on the region to compensate for a frame processing speed of eye-tracking devices, relatively slow to apply the resolution conversion. SRCNN applies to cognitive regions, and typical interpolation applies to other regions to reduce the rendering cost. As a result, the computation is decreased by 90.4059%, and PSNR is higher than the conventional foveated rendering algorithm.