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We study the use of machine learning for visual quality evaluation with comprehensive singular value decomposition (SVD)-based visual features. In this paper, the two-stage process and the relevant work in the existing visual quality metrics are first introduced followed by an in-depth analysis of SVD for visual quality assessment. Singular values and vectors form the selected features for visual quality assessment. Machine learning is then used for the feature pooling process and demonstrated to be effective. This is to address the limitations of the existing pooling techniques, like simple summation, averaging, Minkowski summation, etc., which tend to be ad hoc. We advocate machine learning for feature pooling because it is more systematic and data driven. The experiments show that the proposed method outperforms the eight existing relevant schemes. Extensive analysis and cross validation are performed with ten publicly available databases (eight for images with a total of 4042 test images and two for video with a total of 228 videos). We use all publicly accessible software and databases in this study, as well as making our own software public, to facilitate comparison in future research.