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
this https URL

通过动态深度神经网络辅助的内容感知数据处理管道，我们提出了一种减少模型数量的方法，以提高性能并节省计算资源，并通过编译优化实现更好的 PSNR 和实时性能，在现成的手机上实现了 1.7 倍的加速，节省了高达 1.61 倍的内存消耗。

基于动态算法和编译器协同设计的设备端超分辨率中的数据过拟合

Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm  and Compiler Co-Design

Lossy image compression is pervasively conducted to save communication
bandwidth, resulting in undesirable compression artifacts. Recently, extensive
approaches have been proposed to reduce image compression artifacts at the
decoder side; however, they require a series of architecture-identical models
to process images with different quality, which are inefficient and
resource-consuming. Besides, it is common in practice that compressed images
are with unknown quality and it is intractable for existing approaches to
select a suitable model for blind quality enhancement. In this paper, we
propose a resource-efficient blind quality enhancement (RBQE) approach for
compressed images. Specifically, our approach blindly and progressively
enhances the quality of compressed images through a dynamic deep neural network
(DNN), in which an early-exit strategy is embedded. Then, our approach can
automatically decide to terminate or continue enhancement according to the
assessed quality of enhanced images. Consequently, slight artifacts can be
removed in a simpler and faster process, while the severe artifacts can be
further removed in a more elaborate process. Extensive experiments demonstrate
that our RBQE approach achieves state-of-the-art performance in terms of both
blind quality enhancement and resource efficiency. The code is available at
this https URL

该论文提出了一种适用于压缩图像的资源高效的盲品质增强方法，它在动态深度神经网络中，嵌入了一种早期退出策略，通过逐步提高压缩图像的质量，自动决定终止或继续增强，从而在更简单和更快的进程中去除轻微的失真，同时在更精细的进程中进一步去除严重的失真。