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We introduce novel information-entropic variables—a Point Divergence Gain ( Ω α ( l → m ) ), a Point Divergence Gain Entropy ( I α ), and a Point Divergence Gain Entropy Density ( P α )—which are derived from the Rényi entropy and describe spatio-temporal changes between two consecutive discrete multidimensional distributions. The behavior of Ω α ( l → m ) is simulated for typical distributions and, together with I α and P α , applied in analysis and characterization of series of multidimensional datasets of computer-based and real images.