In Part 1 of this two-part series, we show that the processing of compressed data generally yields decreased computational cost, due to the requirement of fewer operations in the presence of fewer data. We described several compressive transformations in terms of image algebra, a rigorous, concise notation that unifies linear and nonlinear mathematics in the image domain. In this paper, we continue our development of compressive automated target recognition algorithms to include compressive stereo matching. In particular, we elucidate techniques for mapping a cepstrum-based stereo matching algorithm to stereoscopic images represented by the aforementioned compressive formats. Analyses emphasize computational cost, stereo disparity error, and applicability to ATR over various classes of surveillance imagery. Since the study notation, image algebra, has been implemented on numerous sequential and parallel computers, our algorithms are feasible and widely portable.