@Article{FeiYang2020,
author="Fei Yang
and Luis Herranz
and Joost Van de Weijer
and Jose Antonio Iglesias
and Antonio Lopez
and Mikhail Mozerov",
title="Variable Rate Deep Image Compression with Modulated Autoencoder",
journal="IEEE Signal Processing Letters",
year="2020",
volume="27",
pages="331--335",
abstract="Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.",
optnote="LAMP; ADAS; 600.141; 600.120; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3346), last updated on Fri, 07 Jan 2022 12:29:16 +0100",
doi="10.1109/LSP.2020.2970539",
opturl="https://ieeexplore.ieee.org/document/8977394",
file=":http://refbase.cvc.uab.es/files/YHW2020.pdf:PDF"
}