Gosala Kulupana is a postgraduate researcher attached to University of Surrey whose research is based on the fields related to video compression, video communication and signal processing. He has obtained his B.Sc. Engineering honours degree in Electronic and Telecommunication Engineering from University of Moratuwa, Sri Lanka with a First class. Later, he joined Mobitel pvt. Ltd, a leading telecommunication company located in Sri Lanka, as a Radio Network Engineer. He has also worked in a large-scale EU project (action-tv) by contributing to architecture optimizing. Also, during his research career, he did some collaborative work with Wireless Communication Center in University of Oulu.
Parameters Selection at the Encoder for Robust
HEVC Video Transmission,IEEE Transactions on Circuits and Systems for Video Technology IEEE
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 HEVC (High Efficiency Video Coding) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/AVC (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 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-»
relationship which can be used in the RDO (Rate Distortion Optimization) 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 as low as 3.11% whereas the state-of-theart methods can?t 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-ofthe-art error resilient algorithms.