In this paper methods for the interpolation of lost cells in asynchronous-transfer-mode (ATMI networks are studied. It is shown that use of motion-compensated previous frames gives the best results. The quality of the interpolated pictures improves if the motion vectors truly represent the actual motion in the scene. This is only possible with a two-layer coding scheme, where the motion vectors can be delivered to the decoder through the base-layer guaranteed channel. In derivation of the motion vectors at the encoder, use of uncoded input picture frames outperforms the conventional method of motion extraction from the previous coded pictures, despite the lower bit rate of the latter to the former. Depending on the quality of the base layer and the scene activity, the signal-to-noise ratio (SNR) in the cell-loss-interpolated areas can be improved by up to 10 dB.

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Measurements of subjective picture impairment as a function of network loading in a simulated ATM network are reported. The simulation indicated that cells tend to be discarded in bursts, the frequency and severity of which can be related to the loading by a threshold model. The effect of the discards on broadcast-style video, coded using a single-layer H.261-type method, was found to be a function of scene content and movement at the instant of occurrence. If the visibility of cell discards is maintained at or below threshold in worst-case scenes, the study indicated that network loadings around 55% for a multiplex of 16 video sources and around 70% for a multiplex of 48 video sources are achievable.

Texture classification applied to the frame difference signals can be used to design adaptive algorithms for a variety of video applications. In this paper an adaptive block matching motion estimation algorithm based on the interframe texture analysis is presented. The algorithm adaptively changes the size and shape of the search window of each block depending on the values of textural features. The chosen features are extracted from the temporal difference histogram and provide information about the speed of the moving objects and the most likely direction of their motion. Applications of the proposed algorithm to the normal and hierarchical motion estimators are shown. Compared to the known techniques, the presented methods reduce the computational effort by saving unnecessary searches while offering higher performance.

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