In this paper, a novel quality metric to evaluate depth-based synthesized views is proposed. This metric relies on a hybrid approach that uses features extracted in different phases of the image synthesis procedure, namely from the bitstream, from intermediate data produced during the synthesis process, and from the final synthesized view; these features are then combined using support vector regression (SVR). A new dataset of synthesized images, with compression and rendering artifacts, was built and used to develop and assess the proposed metric. The metric performance is compared with conventional full-reference 2D image quality assessment metrics and with quality assessment metrics developed specifically for synthesized images. The experimental results showed that the proposed solution outperforms the considered benchmark metrics, being able to predict the subjective quality scores of the synthesized images with a Pearson correlation coefficient close to 0.9.