To address the problems that existing methods have difficulty in achieving a high compression ratio and low distortion when processing whole-brain data of macaque with high dynamic range, this paper proposes an end-to-end multi-scale compression network based on the U-Net framework. First, the stability of the network is increased and high-frequency information of the image data is preserved by establishing a multi-level controllable jump connection between the compression module and the reconstruction module. Then, the data output by the coding module are processed with straight-through estimation quantization to accelerate the modeling process of the probability model and improve the compression ratio. Experimental results show that the rate-distortion curves of the network on the cellular architecture dataset and the nerve fiber dataset are better than those of other mainstream deep learning methods and the traditional JPEG2000 method. Under a compression ratio of 160, the multi-scale structural similarity index is not less than 0.99.