KEYWORDS: Video, Computer programming, Video coding, Motion models, Analytical research, Forward error correction, Video compression, Signal attenuation, Signal to noise ratio, Performance modeling
Transmitting high-quality, real-time interactive video over lossy networks is challenging because network data loss can severely
degrade video quality. A promising feedback technique for low-latency video repair is Reference Picture Selection (RPS), whereby
the encoder selects one of several previous frames as a reference frame for predictive encoding of subsequent frames. RPS operates in
two different modes: an optimistic policy that uses negative acknowledgements (NACKs) and a more conservative policy that relies
upon positive acknowledgements (ACKs). The choice between RPS NACK and RPS ACK depends on network conditions, such as
round-trip time and loss probability, and on the video content, such as low or high motion. This paper derives two analytical models to
predict the quality of videos (using Peak Signal to Noise Ration, PSNR) with RPS NACK and RPS ACK. These models are used to
study RPS performance under varied network conditions and with different video contents through a series of experiments. Analysis
shows that the best choice of ACK or NACK greatly depends upon the round-trip time and packet loss, and somewhat depends upon
the video content and Group of Pictures (GOP) size. In particular: 1) RPS ACK performs better than RPS NACK when round-trip
times are low; 2) RPS NACK performs better than RPS ACK when the loss rate is low, and RPS ACK performs better than RPS
NACK when the loss rate is high; 3) for a given round-trip time, the loss rate where RPS NACK performs worse than RPS ACK is
higher for low motion videos than it is for high motion videos; 4) videos with RPS NACK always perform no worse than videos
without repair for all GOP sizes; however, 5) below certain GOP sizes, videos without RPS outperform videos with RPS ACK. These
insights derived from our models can help determine appropriate choices for RPS NACK and RPS ACK under various scenarios.
KEYWORDS: Video, Computer programming, Video coding, Video compression, Quality measurement, Semantic video, Data modeling, Video processing, Distance measurement, Motion measurement
Transmitting high-quality, real-time interactive video over lossy networks is challenging because data loss due to the network can severely degrade video quality. A promising feedback technique for low-latency video repair is Reference Picture Selection (RPS), whereby the encoder selects one of several previous frames as a reference frame for predictive encoding of subsequent frames. RPS can operate in two different modes: an optimistic policy that uses negative acknowledgements (NACKs) and a more conservative policy that relies upon positive acknowledgements (ACKs). The choice between RPS ACK mode and NACK mode to some extent depends upon the effects of reference distance on the encoded video quality. This paper provides a systematic study of the effects of reference distance on video quality for a range of video coding conditions. High-quality videos with a wide variety of scene complexity and motion characteristics are selected and encoded using H.264 with a bandwidth constraint and a range of reference distances. Two objective measures of video quality, PSNR and VQM, are analyzed to show that scene complexity and motion characteristics of the video determine the amount of degradation in quality as reference distance increases. In particular, videos with low motion degrade in quality more with an increase in reference distance since they cannot take advantage of the strong similarity between adjacent frames. Videos with high motion do not suffer as much with higher reference distance since the similarity between adjacent frames is already low. The motion characteristics also determine the initial quality under the bandwidth constraint. The data presented should be useful for selecting ACK or NACK mode or for modeling video repair techniques.
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