Compression of encrypted data can be achieved by employing Slepian-Wolf coding (SWC). However, how to efficiently exploit the source dependency in an encrypted colored signal such as an image remains a challenging issue. Previous works incorporate 2D Markov models in the SWC, which is not accurate enough for natural grayscale images; as a result, the compression performance is usually poor. In this paper, we propose to compress the image progressively, such that the decoder can observe a low-resolution version of the image, from which local statistics is learned and used for the decoding of the next resolution level. Good performance is observed both theoretically and experimentally.