Image encryption techniques generally encrypt a secret image into a meaningless noise-like format, which could easily attract attention from attackers who then may try to crack it. On the other hand, image steganography typically embeds secret image data within a cover image, but it inevitably incurs a lot of distortion to the cover image. This makes the secret image data vulnerable to attacks by steganalysis tools. In light of the above, in this paper, we propose a Visually Meaningful Image Encryption (VMIE) scheme via image-to-image reversible transformation based on the Glow model. In this scheme, a secret image is encoded and compressed as a latent vector by the deep compression autoencoder. Then, the latent vector is scrambled and inputted into the Glow model to generate a visually meaningful encrypted image. Extensive experiments demonstrate that the proposed VMIE scheme not only provides desirable security against attacks, but also enables the reconstruction of the original images with negligible quality loss. Codes are available athttps://github.com/AIMS-Group-ZhiliZhou/VMEI.