Learned Iterative Decoding for Lossy Image Compression Systems
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For lossy image compression systems, we develop an algorithm called iterative refinement, to improve the decoder's reconstruction compared with standard decoding techniques. Specifically, we propose a recurrent neural network approach for nonlinear, iterative decoding. Our neural decoder, which can work with any encoder, employs self-connected memory units that make use of both causal and non-causal spatial context information to progressively reduce reconstruction error over a fixed number of steps. We experiment with variations of our proposed estimator and obtain as much as a 0.8921 decibel (dB) gain over the standard JPEG algorithm and a 0.5848 dB gain over a state-of-the-art neural compression model.
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