We define image quality by how accurately an observer, human or otherwise, can perform a given task, such
as determining to which class an image belongs. For detection tasks, the Bayesian ideal observer is the best
observer, in that it sets an upper bound for observer performance, summarized by the area under the receiver
operating characteristic curve. However, the use of this observer is frequently infeasible because of unknown
image statistics, whose estimation is computationally costly. As a result, a channelized ideal observer (CIO) was
investigated to reduce the dimensionality of the data, yet approximate the performance of the ideal observer.
Previously investigated channels include Laguerre Gauss (LG) channels and channels via the singular value
decomposition of the given linear system (SVD). Though both types are highly efficient for the ideal observer,
they nevertheless have the weakness that they may not be as efficient for general detection tasks involving
complex/realistic images; the former is particular to the signal and background shape, and the latter is particular
to the system operator. In this work, we attempt to develop channels that can be applied to a system with
any signal and background type and without knowledge of any characteristics of the system. The method used
is a partial least squares algorithm (PLS), in which channels are chosen to maximize the squared covariance
between images and their classes. Preliminary results show that the CIO with PLS channels outperforms one
with either the LG or SVD channels and very closely approximates ideal-observer performance.