Recently, many deep learning applications have been used on the mobile platform. To deploy them in the mobile platform, the networks should be quantized. The quantization of computer vision networks has been studied well but there have been few studies for the quantization of image restoration networks. In this paper, we studied the effect of the quantization of activations for deep learning network on image quality following previous study for weight quantization for deep learning network. This study is also about the quantization on raw RGBW image demosaicing for 10 bit image while fixing weight bit as 8 bit. Experimental results show that 11 bit activation quantization can sustain image quality at the similar level with floating-point network. Even though the activations bit-depth can be very small bit in the computer vision applications, but image restoration tasks like demosaicing require much more bits than those applications. 11 bit may not fit the general purpose hardware like NPU, GPU or CPU but for the custom hardware it is very important to reduce its hardware area and power as well as memory size.
Quantization of images containing low texture regions, such as sky, water or skin, can produce banding artifacts. As the bitdepth of each color channel is decreased, smooth image gradients are transformed into perceivable, wide, discrete bands. Commonly used quality metrics cannot reliably measure the visibility of such artifacts. In this paper we introduce a visual model for predicting the visibility of both luminance and chrominance banding artifacts in image gradients spanning between two arbitrary points in a color space. The model analyzes the error introduced by quantization in the Fourier space, and employs a purpose-built spatio-chromatic contrast sensitivity function to predict its visibility. The output of the model is a detection probability, which can be then used to compute the minimum bit-depth for which banding artifacts are just-noticeable. We demonstrate that the model can accurately predict the results of our psychophysical experiments.