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Self-Organized Variational Autoencoders for Learned Image Compression

2021-05-25 17:44:20
M. Akın Yılmaz, Onur Keleş, Hilal Güven, A. Murat Tekalp, Junaid Malik, Serkan Kıranyaz

Abstract

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their self-organized variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity. The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality.

Abstract (translated)

URL

https://arxiv.org/abs/2105.12107

PDF

https://arxiv.org/pdf/2105.12107.pdf


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