FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos
This work addresses the problem of enabling real-time super-resolution processing for ultra-HD displays and energy-constrained devices like phones and tablets, representing an incremental improvement by optimizing existing methods.
The paper tackles the high computational cost of super-resolution algorithms for videos by introducing FAST, a framework that accelerates any SR algorithm on compressed videos by exploiting temporal correlations, achieving up to 15x speedup with a minimal visual quality loss of 0.2dB.
State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e.g., 60Mpixels/s for HD video). This paper introduces FAST (Free Adaptive Super-resolution via Transfer), a framework to accelerate any SR algorithm applied to compressed videos. FAST exploits the temporal correlation between adjacent frames such that SR is only applied to a subset of frames; SR pixels are then transferred to the other frames. The transferring process has negligible computation cost as it uses information already embedded in the compressed video (e.g., motion vectors and residual). Adaptive processing is used to retain accuracy when the temporal correlation is not present (e.g., occlusions). FAST accelerates state-of-the-art SR algorithms by up to 15x with a visual quality loss of 0.2dB. FAST is an important step towards real-time SR algorithms for ultra-HD displays and energy constrained devices (e.g., phones and tablets).