The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the signal processing and neural network community. Using this as pattern primitives we aim for source patterns with the highest occurrence probability or highest information. For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that the Independent Principal Components (IPC) in contrast to the Principal Independent Components (PIC) implement the classical demand of Shannon’s rate distortion theory.