PT Journal
AU Fei Yang
   Luis Herranz
   Joost Van de Weijer
   Jose Antonio Iglesias
   Antonio Lopez
   Mikhail Mozerov
TI Variable Rate Deep Image Compression with Modulated Autoencoder
SO IEEE Signal Processing Letters
JI SPL
PY 2020
BP 331
EP 335
VL 27
DI 10.1109/LSP.2020.2970539
AB 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.
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