A Preliminary Comparison Between Compressive Sampling and Anisotropic Mesh-based Image Representation

TL;DR

This paper compares compressed sensing and mesh-based image representation, showing that AMA mesh-based method can achieve better image reconstruction quality at the same sampling density.

Contribution

It provides the first preliminary comparison between CS and AMA mesh-based image representation, highlighting the potential advantages of mesh-based methods.

Findings

AMA representation outperforms CS at the same sample density

Mesh-based representation directly works on image pixels

Further research with advanced algorithms is needed

Abstract

Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself. Although regular images are not sparse on their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. However, an alternative approach, adaptive sampling such as mesh-based image representation (MbIR), has not attracted as much attention. MbIR works directly on image pixels and represents the image with fewer points using a triangular mesh. In this paper, we perform a preliminary comparison between the CS and a recently developed MbIR method, AMA representation. The results demonstrate that, at the same sample density, AMA representation can provide better reconstruction quality than CS based on the tested algorithms. Further investigation with recent algorithms is needed to perform a thorough comparison.