User Generated Content (UGC) refers to media generated by users for end-consumers that represent most of the media exchange on social media. UGC is subject to acquisition and transmission limitations that disable access to the pristine, i.e., perfect source content. Evaluating their quality, especially with current pre-and post-processing algorithms or filters, is a major issue for most off-the-shelf full-reference quality metrics. We propose to conduct a benchmark on existing full-reference, non-reference, and aesthetic quality metrics for UGC with special effects. We aim to identify the challenges posed by both UGC and filtering. We then propose a new combination of metrics tailored to enhanced and filtered UGC, which reaches a trade-off between complexity and accuracy.