We examine the visual effect of whole-frame loss by different decoders. Whole-frame losses are introduced in H.264/AVC compressed videos which are then decoded by two different decoders with different common concealment effects: frame copy and frame interpolation. The videos are seen by human observers who respond to each glitch they spot. We found that about 39% of whole-frame losses of B frames are not observed by any of the subjects, and over 58% of the B frame losses are observed by 20% or fewer of the subjects. Using simple predictive features that can be calculated inside a network node with no access to the original video and no pixel level reconstruction of the frame, we develop models that can predict the visibility of whole B frame losses. The models are then used in a router to predict the visual impact of a frame loss and perform intelligent frame dropping to relieve network congestion. Dropping frames based on their visual scores proves superior to random dropping of B frames.