Internet streaming has experienced tremendous growth in the past few years, and continues to advance at a rapid
pace. Streaming
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.