  We propose a unified signal compression framework that uses a generative
adversarial network (GAN) to compress heterogeneous signals. The compressed
signal is represented as a latent vector and fed into a generator network that
is trained to produce high quality realistic signals that minimize a target
objective function. To efficiently quantize the compressed signal,
non-uniformly quantized optimal latent vectors are identified by iterative
back-propagation with alternating direction method of multipliers (ADMM)
optimization performed for each iteration. The performance of the proposed
signal compression method is assessed using multiple metrics including PSNR and
MS-SSIM for image compression and also PESR, Kaldi, LSTM, and MLP performance
for speech compression. Test results show that the proposed work outperforms
recent state-of-the-art hand-crafted and deep learning-based signal compression
methods.
