Conventional video traces (which characterize the video encoding frame sizes in bits and frame quality in PSNR) are limited to evaluating loss-free video transmission. To evaluate robust video transmission schemes for lossy network transport, generally experiments with actual video are required. To circumvent the need for experiments with actual videos, we propose in this paper an advanced video trace framework. The two main components of this framework are (i) advanced video traces which combine the conventional video traces with a parsimonious set of visual content descriptors, and (ii) quality prediction schemes that based on the visual content descriptors provide an accurate prediction of the quality of the reconstructed video after lossy network transport. We conduct extensive evaluations using a perceptual video quality metric as well as the PSNR in which we compare the visual quality predicted based on the advanced video traces with the visual quality determined from experiments with actual video. We find that the advanced video trace methodology accurately predicts the quality of the reconstructed video after frame losses.

Abstract-A variety of error resilience and scalable coding techniques have recently been proposed to facilitate the delivery of video over best-effort networks; a common drawback of these techniques is reduced compression efficiency. Also, MPEG-7 descriptors have recently been developed for the purpose of indexing. In this paper, we propose to employ MPEG-7 descriptors to improve the quality of the video delivered over best-effort networks. In particular, we propose a video transmission system that uses the motion activity descriptors to ensure robust video transmission. A novel motion activity extraction technique is proposed, which relies on a neural network approach. By considering several low-level visual features, our proposed extraction approach achieves high consistency with subjective evaluations of motion activities. In order to demonstrate the benefits of the proposed transmission system, we develop a selective packet dropping scheme that can be applied in case of network congestion. Simulations demonstrate that the reconstruction quality of the proposed congestion scheme can surpass conventional schemes by 1.2 dB. The network performance of the proposed transmission system when video sequences are coded into single layer or scalable layers is presented. We also present a transcoding scheme that achieves the optimal reconstructed quality by exploiting the motion activities of the underlying video sequence.

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