  We provide a large image parameter dataset extracted from the Solar Dynamics
Observatory (SDO) mission's AIA instrument, for the period of January 2011
through the current date, with the cadence of six minutes, for nine wavelength
channels. The volume of the dataset for each year is just short of 1 TiB.
Towards achieving better results in the region classification of active regions
and coronal holes, we improve upon the performance of a set of ten image
parameters, through an in depth evaluation of various assumptions that are
necessary for calculation of these image parameters. Then, where possible, a
method for finding an appropriate settings for the parameter calculations was
devised, as well as a validation task to show our improved results. In
addition, we include comparisons of JP2 and FITS image formats using supervised
classification models, by tuning the parameters specific to the format of the
images from which they are extracted, and specific to each wavelength. The
results of these comparisons show that utilizing JP2 images, which are
significantly smaller files, is not detrimental to the region classification
task that these parameters were originally intended for. Finally, we compute
the tuned parameters on the AIA images and provide a public API
(http://dmlab.cs.gsu.edu/dmlabapi) to access the dataset. This dataset can be
used in a range of studies on AIA images, such as content-based image retrieval
or tracking of solar events, where dimensionality reduction on the images is
necessary for feasibility of the tasks.
