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We conduct an objective experiment in which Video Quality Metric (VQM) scores are computed on compressed video GOPs following fixed-sized IP packet loss, and then construct a network-based model to predict these VQM scores. The model is created for H.264 SDTV videos using a no-reference method, meaning that we only use the information from the bitstream but have no access to the original video. The...
We conduct subjective experiments on visual quality following packet loss, and then construct models to predict these visual importance scores. The models are fully self-contained at the packet level, meaning that they use only information within one packet to predict the importance of that packet, requiring no frame-level reconstruction nor any information on the reference frame. Models are created...
For videos transmitted in an error-prone network, it is necessary to protect the source bitstream. Based on our packet loss visibility model, we minimize the end-to-end video quality degradation when transmitted in an AWGN channel using rate-compatible punctured convolutional codes for a given channel rate budget. We transform the original problem into a binary-decision problem, then we solve this...
Our work builds a general visibility model of video packets which is applicable to various types of GOP (group of pictures). The data used for analysis and building the model come from three subjective experiment sets with different encoding and decoding parameters on H.264 and MPEG-2 videos. We consider factors not only within a packet but also across its vicinity to account for possible temporal...
When video packets are lost in congested networks, one loss pattern creates a different visual impact than another. We conduct a subjective experiment with H.264 videos and conclude that isolated losses are better than bursty losses in terms of perceptual video quality. A network-implementable video quality model is developed for a router to drop packets so as to achieve good visual quality.
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