Year: 2024
Type: article
Abstract: 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.
Author: Kenyon, Garrett [Los Alamos National Laboratory]
Institution: Los Alamos National Laboratory
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2963266122