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Along with the rapid increase in the availability of high-quality video formats such as high definition (HD), ultra HD, and high dynamic range, a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec high efficiency video coding (HEVC) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/advanced video coding, the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers’ perceived quality degradation prior to transmitting HEVC coded videos over an error-prone network, and to include error resilient features so as to minimize the adverse effects of those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP– $\lambda $ relationship which can be used in the rate-distortion optimization (RDO) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames, and the clipping operations are accounted, and consequently, the model is capable of minimizing the prediction error to as low as 3.11%, whereas the state-of-the-art methods cannot reach below 20.08%, under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%–43.59% improvement in the BD-rate compared to the state-of-the-art error resilient algorithms.

End User Video Quality Prediction and Coding Parameters Selection at the Encoder for Robust HEVC Video Transmission