# Learned Neural Iterative Decoding for Lossy Image Compression Systems

arXiv: https://arxiv.org/abs/1803.05863 · HTML: https://mlsanity.com/paper/1803.05863/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://mlsanity.com/paper/2201.09240/) (2022)
- [An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems](https://mlsanity.com/paper/2201.11782/) (2022)
- [Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder](https://mlsanity.com/paper/2201.11795/) (2022)

## Abstract (arXiv)

For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques. Specifically, we propose a recurrent neural network approach for nonlinear, iterative decoding. Our decoder, which works with any encoder, employs self-connected memory units that make use of causal and non-causal spatial context information to progressively reduce reconstruction error over a fixed number of steps. We experiment with variants of our estimator and find that iterative refinement 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 0.871 decibel (dB) gain over JPEG, a 1.095 dB gain over JPEG 2000, and a 0.971 dB gain over a competitive neural model.