  For lossy image compression, we develop a neural-based system which learns a
nonlinear estimator for decoding from quantized representations. The system
links two recurrent networks that \help" each other reconstruct same target
image patches using complementary portions of spatial context that communicate
via gradient signals. This dual agent system builds upon prior work that
proposed the iterative refinement algorithm for recurrent neural network
(RNN)based decoding which improved image reconstruction compared to standard
decoding techniques. Our approach, which works with any encoder, neural or
non-neural, This system progressively reduces image patch reconstruction error
over a fixed number of steps. Experiment with variants of RNN memory cells,
with and without future information, find that our model consistently creates
lower distortion images of higher perceptual quality compared to other
approaches. Specifically, on the Kodak Lossless True Color Image Suite, we
observe as much as a 1:64 decibel (dB) gain over JPEG, a 1:46 dB gain over JPEG
2000, a 1:34 dB gain over the GOOG neural baseline, 0:36 over E2E (a modern
competitive neural compression model), and 0:37 over a single iterative neural
decoder.
