  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.
