Internet streaming has experienced tremendous growth in the past few years, and continues to advance at a rapid
now accounts for over 60% of internet traffic and is expected to quadruple over the next five years.
Video delivery quality depends critically on available network bandwidth. Due to bandwidth limitations, most video sources are compressed, resulting in image artifacts, noise, and blur. Quality is also degraded by routine image upscaling, which is required to match the very high pixel density of newer mobile devices.
The upscaling community has provided us with many fundamental advances in video and image upscaling, from classic methods such as Nearest-Neighbor, Linear and Lanczos resampling. However, no fundamentally new methods have been introduced in over 20 years. Also, traditional algorithm-based upscaling methods lack fine detail and cannot remove defects and compression artifacts.
All of this is changing thanks to the Deep Learning revolution. We now have a whole new class of techniques for state-of-the-art upscaling, called Deep Learning Super Resolution (DLSR).
The following shows different examples of X4 upsampling using our trained DLSR model compared with Lanczos upscaling.