PatchSVD: A Non-uniform SVD-based Image Compression Algorithm
This addresses storage challenges for image data users, but it is incremental as it builds on existing SVD methods.
The paper tackles the problem of efficient image compression for managing storage costs by proposing PatchSVD, a region-based lossy compression technique based on SVD, which outperforms SVD-based compression on three metrics and shows preferable artifacts compared to JPEG and SVD in some cases.
Storing data is particularly a challenge when dealing with image data which often involves large file sizes due to the high resolution and complexity of images. Efficient image compression algorithms are crucial to better manage data storage costs. In this paper, we propose a novel region-based lossy image compression technique, called PatchSVD, based on the Singular Value Decomposition (SVD) algorithm. We show through experiments that PatchSVD outperforms SVD-based image compression with respect to three popular image compression metrics. Moreover, we compare PatchSVD compression artifacts with those of Joint Photographic Experts Group (JPEG) and SVD-based image compression and illustrate some cases where PatchSVD compression artifacts are preferable compared to JPEG and SVD artifacts.