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Deep convolutional neural network (DCNN) based image codecs, consisting of encoder, quantizer and decoder, have achieved promising image compression results. The major challenge in learning these DCNN models lies in the joint optimization of the encoder, quantizer and decoder, as well as the adaptivity to the input images. In this paper, we proposed a DCNN architecture for image compression, where the encoder, quantizer and decoder are jointly learned. Specifically, a fully convolutional vectordoi:10.1109/access.2019.2934731 fatcat:qnrr7qblvfbbdoyntkqz4ucb3u