Image Compression: Sparse Coding vs. Bottleneck Autoencoders

TL;DR

This paper compares sparse coding and bottleneck autoencoders for image compression, showing that sparse coding yields higher quality reconstructions and better perceptual metrics despite higher computational costs.

Contribution

It demonstrates that sparse coding outperforms bottleneck autoencoders in image quality and perceptual metrics at the same compression level, highlighting its potential despite computational challenges.

Findings

Sparse coding produces visually superior reconstructed images.

Higher PSNR and SSIM scores with sparse coding.

Perceptual metrics favor sparse coding over autoencoders.

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.