With the development of high-definition display devices, the practical
scenario of Super-Resolution (SR) usually needs to super-resolve large input
like 2K to higher resolution (4K/8K). To reduce the computational and memory
cost, current methods first split the large input into local patches and then
merge the SR patches into the output. These methods adaptively allocate a
subnet for each patch. Quantization is a very important technique for network
acceleration and has been used to design the subnets. Current methods train an
MLP bit selector to determine the propoer bit for each layer. However, they
uniformly sample subnets for training, making simple subnets overfitted and
complicated subnets underfitted. Therefore, the trained bit selector fails to
determine the optimal bit. Apart from this, the introduced bit selector brings
additional cost to each layer of the SR network. In this paper, we propose a
novel method named Content-Aware Bit Mapping (CABM), which can remove the bit
selector without any performance loss. CABM also learns a bit selector for each
layer during training. After training, we analyze the relation between the edge
information of an input patch and the bit of each layer. We observe that the
edge information can be an effective metric for the selected bit. Therefore, we
design a strategy to build an Edge-to-Bit lookup table that maps the edge score
of a patch to the bit of each layer during inference. The bit configuration of
SR network can be determined by the lookup tables of all layers. Our strategy
can find better bit configuration, resulting in more efficient mixed precision
networks. We conduct detailed experiments to demonstrate the generalization
ability of our method. The code will be released.

本文提出了一种名为内容感知比特映射的新方法，可以在训练期间学习每个层的比特选择器，构建适用于超分辨率网络的边缘到比特查找表，通过该策略可以找到更好的比特配置结果，使得混合精度网络更加高效。