Image Compression: Sparse Coding vs. Bottleneck Autoencoders

Bottleneck autoencoders have been actively researched as a solution to image compression tasks. However, we observed that bottleneck autoencoders produce subjectively low quality reconstructed images. In this work, we explore the ability of sparse coding to improve reconstructed image quality for th...

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Bibliographic Details
Published in:2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) pp. 17 - 20
Main Authors: Watkins, Yijing, Iaroshenko, Oleksandr, Sayeh, Mohammad, Kenyon, Garrett
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2018
Subjects:
Bottleneck Autoencoders
Convolutional codes
Feature Perceptual Loss
Generators
Image coding
Image Compression
Image quality
Image reconstruction
Neurons
Sparse Coding
Thumbnails
Training
ISSN:2473-3598
Online Access:Get full text
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Summary:Bottleneck autoencoders have been actively researched as a solution to image compression tasks. However, we observed that bottleneck autoencoders produce subjectively low quality reconstructed images. In this work, we explore the ability of sparse coding to improve reconstructed image quality for the same degree of compression. We observe that sparse image compression produces visually superior reconstructed images and yields higher values of pixel-wise measures of reconstruction quality (PSNR and SSIM) compared to bottleneck autoencoders. In addition, we find that using alternative metrics that correlate better with human perception, such as feature perceptual loss and the classification accuracy, sparse image compression scores up to 18.06% and 2.7% higher, respectively, compared to bottleneck autoencoders. Although computationally much more intensive, we find that sparse coding is otherwise superior to bottleneck autoencoders for the same degree of compression.
ISSN:2473-3598
DOI:10.1109/SSIAI.2018.8470336