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The quality assessment of multi-crystalline and high-performance multi-crystalline silicon wafers during incoming inspection of solar cell production requires a reproducible description of the relevant material defects and a classification scheme that is capable to rate as-cut wafers from unknown manufacturers. Both needs are addressed in this work. We introduce an image processing framework that allows the various types of crystallization-related defects visible in photoluminescence images to be detected quantitatively and thus enables a complete wafer description in terms of defects. The importance of different features within this defect characteristic is weighted by predicting the open-circuit voltage of solar cells with aluminum back-surface field as well as passivated emitter and rear cells with a stepwise extension of the model. The resulting robust classification scheme is successfully evaluated on a set of 7500 wafers, which represents the whole spectrum of material types and qualities that are currently available at the market. A comparison of defect signatures in high-performance multi-crystalline and standard multi-crystalline silicon materials underlines the relevance of additional features. As a result of this paper, we show that a regularized version of multi-linear regression models for quality prediction can optimize simpler linear models by adding selected features to the defect characteristic leading to mean absolute prediction errors of 2.2 mV for solar cells with aluminum back-surface field and 2.9 mV for passivated emitter and rear cells solar cells in a true blind test