PROJECT TITLE :
Frame Interpolation for Cloud-Based Mobile Video Streaming
Cloud-based mostly High Definition (HD) video streaming is turning into well-liked day-to-day. On one hand, it is vital for each end users and large storage servers to store their huge amount of data at different locations and servers. On the opposite hand, it's turning into a huge challenge for network service providers to produce reliable connectivity to the network users. There have been many studies over cloud-primarily based video streaming for Quality of Expertise (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which have an effect on the user feedback over cloud-primarily based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver facet to estimate the lost data. This paper proposes a time-economical and quality-oriented EC technique. The proposed method considers H.265/HEVC primarily based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis within the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To spice up-up the search process for the lost MVs, a larger block size and looking out in parallel are both thought of. The simulation results clearly show that our proposed methodology outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.five dB and Frame Copy (FC) by up to twelve dB at a packet loss rate of 1%, 3%, and 5% with totally different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 178eight seconds.
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