Image compression, which aims to represent an image with less storage space, is a classical problem in image processing. Recently, by training an encoder-quantizer-decoder network, deep convolutional neural networks (CNNs) have achieved promising results in image compression. As a nondifferentiable part of the compression system, quantizer is hard to be updated during the network training. Most of existing deep image compression methods adopt a uniform rounding function as the quantizer, which however restricts the capability and flexibility of CNNs in compressing complex image structures. In this paper, we present an iterative nonuniform quantization scheme for deep image compression. More specifically, we alternatively optimize the quantizer and encoder-decoder. When the encoder-decoder is fixed, a non-uniform quantizer is optimized based on the distribution of representation features. The encoder-decoder network is then updated by fixing the quantizer. Extensive experiments demonstrate the superior PSNR index of the proposed method to existing deep compressors and JPEG2000.