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For lossy image compression, we develop a neural-based system which learns anonlinear estimator for decoding from quantized representations. The systemlinks two recurrent networks that \help" each other reconstruct same targetimage patches using complementary portions of spatial context that communicatevia gradient signals. This dual agent system builds upon prior work thatproposed the iterative refinement algorithm for recurrent neural network(RNN)based decoding which improved image reconstruction compared to standarddecoding techniques. Our approach, which works with any encoder, neural ornon-neural, This system progressively reduces image patch reconstruction errorover a fixed number of steps. Experiment with variants of RNN memory cells,with and without future information, find that our model consistently createslower distortion images of higher perceptual quality compared to otherapproaches. Specifically, on the Kodak Lossless True Color Image Suite, weobserve as much as a 1:64 decibel (dB) gain over JPEG, a 1:46 dB gain over JPEG2000, a 1:34 dB gain over the GOOG neural baseline, 0:36 over E2E (a moderncompetitive neural compression model), and 0:37 over a single iterative neuraldecoder.

Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication