In this study, we present a metainformation extraction pipeline and a Proof of Concept (PoC) implementation of a recognizer capable of classifying video titles, series titles as well as video genres from encrypted video streams. We show in a promising evaluation, using the Netflix and SVT Play catalogues as examples, that our PoC is capable of learning abstract data from the packet bursts visible in DASH encrypted streams (such as if a video fingerprint is rather a Drama or a Romance movie). This is, to the best of our knowledge, the first demonstration of successful extraction of video metainformation from coarse-grain encrypted network packet traces. While advocating updates in the DASH protocol in order to preserve viewers' privacy, our results also pave the way to future computer forensics systems capable of successful content classification over encrypted channels.