Apparent Resolution Enhancement for Motion Videos
In this work we increase the apparent resolution of videos when viewed on a high-refresh rate display by making use of perceptual properties of the visual system. We achieve this enhancement by exploiting the viewer’s natural tendency to track moving objects in videos which causes the screen pixels to be projected at different sub-pixel offsets onto the retina. We estimate the eye motion using optical flow and use it to compute multiple low-resolution frames for each input frame. By watching these new frames at a high frame-rate, the viewer’s eyes integrate them over time and merges them into a single perceived frame with a denser pixel layout. In this work we also advance the existing approaches for resolution enhancement in the following ways. We combine current display resolution enhancement with super-resolution methods to enhance input videos that are at the display resolution. We derive a new perceived video model that accounts for actual camera sensor and display pixel shapes in order to achieve optimal enhancement. We analyze the degeneracies that certain motion velocities introduce to super-resolution and resolution enhancement, and offer algorithmic solutions for handling these scenarios as well as other difficulties that arise when dealing with the optical flow of natural videos. A user study finds that our approach achieves a noticeable increase in the apparent resolution for videos even when viewed on regular hardware (60Hz), and further enhances resolution when viewed on higher refresh rate displays (120Hz).