Image Compression: Sparse Coding vs. Bottleneck Autoencoders
This addresses image compression quality for applications requiring high-fidelity reconstructions, but it is incremental as it compares existing methods.
The paper tackles the problem of low-quality reconstructed images in bottleneck autoencoders for image compression by exploring sparse coding, finding that it produces visually superior images with higher PSNR and SSIM scores, and up to 18.06% and 2.7% improvements in perceptual metrics.
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.