Optimized Latent Features for Deep Image Compression | IEEE Conference Publication | IEEE Xplore

Optimized Latent Features for Deep Image Compression


Abstract:

The work presented in this paper is focused on deep image compression where a neural network architecture is used to extract the small-sized latent features which encodes...Show More

Abstract:

The work presented in this paper is focused on deep image compression where a neural network architecture is used to extract the small-sized latent features which encodes the full information of the input image. A common drawback in such system is low quality of the reconstructed image. This paper aims to alleviate this drawback by augmenting the latent feature using an optimization strategy. The values in a given latent features are updated iteratively to produce an output image with lower reconstruction loss. The proposed method has been evaluated MNIST dataset where the results shows that it could provide significant gain compared to the reconstruction results which are generated from the non optimized latent features.
Date of Conference: 25-27 June 2019
Date Added to IEEE Xplore: 27 December 2019
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Conference Location: Richmond, VA, USA

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