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.

翻译：随着高清显示设备的发展，超分辨率（SR）的实际场景通常需要将大输入（如2K）超分至更高分辨率（4K/8K）。为降低计算与内存开销，现有方法首先将大输入分割为局部图像块，随后将SR图像块合并为输出，并为每个图像块自适应分配子网络。量化作为网络加速的关键技术，已被用于子网络设计。当前方法通过训练MLP位选择器为每层确定合适位宽，但均匀采样子网络进行训练导致简单子网络过拟合而复杂子网络欠拟合，使得训练后的位选择器无法确定最优位宽。此外，引入的位选择器为SR网络每层带来额外开销。本文提出名为内容感知位映射（CABM）的新方法，该方法可在不造成任何性能损失的前提下消除位选择器。CABM在训练期间同样为每层学习位选择器，训练后我们分析输入图像块的边缘信息与各层位宽之间的关系，发现边缘信息可作为所选位宽的有效度量指标。为此，我们设计了一种构建边缘-位查找表的策略，该表在推理阶段将图像块的边缘分数映射至各层位宽。SR网络的位宽配置可由所有层的查找表共同确定。该策略能寻找更优位宽配置，从而构建更高效的混合精度网络。我们通过详细实验证明了方法的泛化能力，相关代码将公开发布。