  Deep neural networks (DNNs) are frequently employed in a variety of computer
vision applications. Nowadays, an emerging trend in the current video
distribution system is to take advantage of DNN's overfitting properties to
perform video resolution upscaling. By splitting videos into chunks and
applying a super-resolution (SR) model to overfit each chunk, this scheme of SR
models plus video chunks is able to replace traditional video transmission to
enhance video quality and transmission efficiency. However, many models and
chunks are needed to guarantee high performance, which leads to tremendous
overhead on model switching and memory footprints at the user end. To resolve
such problems, we propose a Dynamic Deep neural network assisted by a
Content-Aware data processing pipeline to reduce the model number down to one
(Dy-DCA), which helps promote performance while conserving computational
resources. Additionally, to achieve real acceleration on the user end, we
designed a framework that optimizes dynamic features (e.g., dynamic shapes,
sizes, and control flow) in Dy-DCA to enable a series of compilation
optimizations, including fused code generation, static execution planning, etc.
By employing such techniques, our method achieves better PSNR and real-time
performance (33 FPS) on an off-the-shelf mobile phone. Meanwhile, assisted by
our compilation optimization, we achieve a 1.7$\times$ speedup while saving up
to 1.61$\times$ memory consumption. Code available in
https://github.com/coulsonlee/Dy-DCA-ECCV2024.
