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Página principal > Artículos > Artículos publicados > Variable rate deep image compression with modulated autoencoder |
Fecha: | 2020 |
Descripción: | 5 pàg. |
Resumen: | 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. |
Ayudas: | European Commission 665919 Agencia Estatal de Investigación RTI2018-102285-A-I00 Agencia Estatal de Investigación TIN2017-88709-R Agencia Estatal de Investigación TIN2016-79717-R |
Derechos: | Tots els drets reservats. |
Lengua: | Anglès |
Documento: | Article ; recerca ; Versió acceptada per publicar |
Materia: | Bit rate ; Decoding ; Training ; Image coding ; Distortion ; Quantization (signal) ; Adaptation models |
Publicado en: | IEEE signal processing letters, Vol. 27 (2020) , p. 331-335, ISSN 1070-9908 |
Postprint 6 p, 3.0 MB |