Conventional end-to-end distortion models for videos measure the overall distortion based on independent estimations\nof the source distortion and the channel distortion. However, they are not correlating well with the perceptual\ncharacteristics where there is a strong inter-relationship among the source distortion, the channel distortion, and\nthe video content. As most compressed videos are represented to human users, perception-based end-to-end\ndistortion model should be developed for error-resilient video coding. In this paper, we propose a structural similarity\n(SSIM)-based end-to-end distortion model to optimally estimate the content-dependent perceptual distortion due to\nquantization, error concealment, and error propagation. Experiments show that the proposed model brings a better\nvisual quality for H.264/AVC video coding over packet-switched networks.
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