  In this paper, we present a generative adversarial network framework that
generates compressed images instead of synthesizing raw RGB images and
compressing them separately. In the real world, most images and videos are
stored and transferred in a compressed format to save storage capacity and data
transfer bandwidth. However, since typical generative adversarial networks
generate raw RGB images, those generated images need to be compressed by a
post-processing stage to reduce the data size. Among image compression methods,
JPEG has been one of the most commonly used lossy compression methods for still
images. Hence, we propose a novel framework that generates JPEG compressed
images using generative adversarial networks. The novel generator consists of
the proposed locally connected layers, chroma subsampling layers, quantization
layers, residual blocks, and convolution layers. The locally connected layer is
proposed to enable block-based operations. We also discuss training strategies
for the proposed architecture including the loss function and the
transformation between its generator and its discriminator. The proposed method
is evaluated using the publicly available CIFAR-10 dataset and LSUN bedroom
dataset. The results demonstrate that the proposed method is able to generate
compressed data with competitive qualities. The proposed method is a promising
baseline method for joint image generation and compression using generative
adversarial networks.
