Development of the best prediction matrix mode algorithm forintra prediction in h.264 encoder

  • Authors

    • Sara Hamdy Benha Faculty of Engineering, Benha University- Egypt
    • Abdelhalim Zekry Faculty of Engineering, Ain Shams University –Egypt
    • Wael A. Mohamed Benha Faculty of Engineering, Benha University- Egypt
    2018-12-05
    https://doi.org/10.14419/ijet.v7i4.19497
  • Best Prediction Matrix Mode(BPMM), Compression Efficiency, H.264/AVC, Huffman Coding, Intra Prediction.

  • Abstract

    The video compression techniques began to impose themselves on the ground in recent years, as it can provide sending the videos using less transmission bandwidth or stored in less storage space. One of these innovative compression techniques is the H.264 standard which has huge significance in terms of technology. Thus attracted our interest as researchers since a few years ago and prompted us to propose a new efficient intra prediction scheme and it has been termed the Best Prediction Matrix Mode (BPMM). This novel prediction technique had achieved impressive results for each of compression ratios, bit rates, and PSNR. In this paper, we discuss how we can develop our predic-tion scheme to mitigate some of its major challenges concerning the relatively large encoding overhead and make it more realizable. Namely using entropy coding, the encoding overhead could be appreciably reduced to the level of the conventional intracoding techniques. We have utilized hardware description language VHDL to evaluate the performance of our proposed prediction scheme. The results show that superior interceding performance could be achieved without scarifying space for encoding overhead. We would like to recommend our intra-prediction algorithm, BPMM, in the ITU reference design of H.264.

     

     


     

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  • How to Cite

    Hamdy, S., Zekry, A., & A. Mohamed, W. (2018). Development of the best prediction matrix mode algorithm forintra prediction in h.264 encoder. International Journal of Engineering & Technology, 7(4), 3847-3853. https://doi.org/10.14419/ijet.v7i4.19497

    Received date: 2018-09-11

    Accepted date: 2018-10-08

    Published date: 2018-12-05