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Author (up) Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov edit   pdf
url  doi
  Title Variable Rate Deep Image Compression with Modulated Autoencoder Type Journal Article
  Year 2020 Publication IEEE Signal Processing Letters Abbreviated Journal SPL  
  Volume 27 Issue 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.  
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  Area Expedition Conference  
  Notes LAMP; ADAS; 600.141; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ YHW2020 Serial 3346  
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