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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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Peak Signal-to-Noise Ratio (PSNR) is widely used as a video quality metric or performance indicator. Some studies have indicated that it correlates poorly with subjective quality, whilst others have used it on the basis that it provides a good correlation with subjective data. Existing literature seems to provide conflicting evidence of the accuracy of PSNR as a video quality metric. Based on experimental results, we explain a scenario where PSNR provides a reliable indication of the variation of subjective video quality and scenarios where PSNR is not a reliable video quality metric. We show that PSNR follows a monotonic relationship with subjective quality in the case of full frame rate encoding when the video content and codec are fixed. We provide evidence that PSNR becomes an unreliable and inaccurate quality metric when several videos with different content are jointly assessed. Furthermore, PSNR is inaccurate in measuring video quality of a video content encoded at different frame rates because it is not capable of assessing the perceptual trade-off between the spatial and temporal qualities. Finally, where PSNR is not a reliable video quality metric across different video contents and frame rates, we show that a perceptual video model recently approved by the International Telecommunication Union (ITU) provides quality predictions highly correlating with subjective scores even if different video scenes coded at different frame rates are considered in the test set.