IEEE Trans Image Process - Network-based H.264/AVC whole frame loss visibility model and frame dropping methods.

Tópicos

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Resumo

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 which can be calculated inside a network node with no access to the original video and no pixel level reconstruction of the frame, we developed models which 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.

Resumo Limpo

examin visual effect whole frame loss differ decod whole frame loss introduc havc compress video decod two differ decod differ common conceal effect frame copi frame interpol video seen human observ respond glitch spot found whole frame loss b frame observ subject b frame loss observ fewer subject use simpl predict featur can calcul insid network node access origin video pixel level reconstruct frame develop model can predict visibl whole b frame loss model use router predict visual impact frame loss perform intellig frame drop reliev network congest drop frame base visual score prove superior random drop b frame

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