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Error concealment for cloud-based and scalable video coding of hd videos

- Usman, Muhammad, He, Xiangjian, Lam, Kin-Man, Xu, Min, Bokhari, Syed, Chen, Jinjun, Jan, Mian

  • Authors: Usman, Muhammad , He, Xiangjian , Lam, Kin-Man , Xu, Min , Bokhari, Syed , Chen, Jinjun , Jan, Mian
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE transactions on cloud computing Vol. 7, no. 4 (2019), p. 975-987
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  • Description: The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.

Time-frequency filter bank: A simple approach for audio and music separation

- Yang, Ning, Usman, Muhammad, He, Xiangjian, Jan, Mian, Zhang, Liming

  • Authors: Yang, Ning , Usman, Muhammad , He, Xiangjian , Jan, Mian , Zhang, Liming
  • Date: 2017
  • Type: Text , Journal article
  • Relation: IEEE access Vol. 5, no. (2017), p. 27114-27125
  • Full Text: false
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  • Description: Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.