  Model quantization can reduce the model size and computational latency, it
has become an essential technique for the deployment of deep neural networks on
resourceconstrained hardware (e.g., mobile phones and embedded devices). The
existing quantization methods mainly consider the numerical elements of the
weights and activation values, ignoring the relationship between elements. The
decline of representation ability and information loss usually lead to the
performance degradation. Inspired by the characteristics of images in the
frequency domain, we propose a novel multiscale wavelet quantization (MWQ)
method. This method decomposes original data into multiscale frequency
components by wavelet transform, and then quantizes the components of different
scales, respectively. It exploits the multiscale frequency and spatial
information to alleviate the information loss caused by quantization in the
spatial domain. Because of the flexibility of MWQ, we demonstrate three
applications (e.g., model compression, quantized network optimization, and
information enhancement) on the ImageNet and COCO datasets. Experimental
results show that our method has stronger representation ability and can play
an effective role in quantized neural networks.
