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Complexity reduction of versatile video coding standard: a deep learning approach

机译:多功能视频编码标准的复杂性降低:深度学习方法

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The new video coding standard, known as versatile video coding (VVC) is projected to be concluded by the end of 2020. This standard is conducted mainly to address 8k videos and emerging applications such as 360 deg and high dynamic range. Intraprediction is a part of the prediction step in the video coding that exploits spatial redundancy. This module has been improved, compared to the high-efficiency video coding (HEVC), by increasing the set of angular intraprediction modes (IPM) from 33 to 65 to model directional textures more accurately. Moreover, a quadtree plus binary tree (QTBT) structure replaced the QT of the HEVC. These improvements targeting at enhancing the coding efficiency resulted in significant coding complexity, especially in terms of encoding time. This paper fits into this context. It evokes the optimizations of the intramode and coding unit size decisions using statistical methods of fast decision and deep learning. A fast intramode decision algorithm is proposed for the different binary depths of the QTBT structure. Thus, an optimization by deep learning for square blocks is also included. Results show that the combinations of these two approaches can significantly reduce the complexity of the VVC encoder. Under the all intra (AI) configuration, a reduction of about 61.04% of the intraencoding time is achieved while maintaining an acceptable rate distortion performance. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.2.023002]Video traffic is continuing to grow at a huge rate. According to a Cisco study,1 video consumption will surpass 80% of global IP traffic by 2022. Unsurprisingly, the emerging applications, such as 360 deg and high dynamic range videos, have rapidly gained great attention from video consumers and further advanced the shareability of video content. The foreseeable future will also attest to the dominance of beyond ultrahigh definition qualities and high frame rate videos. Due to this rapid evolution, the need for higher coding efficiency than that of the current standard
机译:新的视频编码标准,称为多功能视频编码(VVC)被投影为在2020年底结束。本标准主要用于解决8K视频和新兴应用,如360°和高动态范围。 Intreapiction是用于利用空间冗余的视频编码中预测步骤的一部分。与高效视频编码(HEVC)相比,该模块得到了改进,通过增加33到65的角度内读取模式(IPM),更准确地模拟定向纹理。此外,Quadtree加二叉树(QTBT)结构替换了HEVC的Qt。靶向增强编码效率的这些改进导致显着的编码复杂性,尤其是在编码时间方面。本文适合这种背景。它唤起了使用快速决策和深度学习的统计方法的intramode和编码单元大小决策的优化。提出了一种快速intramode决策算法,用于QTBT结构的不同二进制深度。因此,还包括深度学习方块的优化。结果表明,这两种方法的组合可以显着降低VVC编码器的复杂性。在所有帧内(AI)配置下,在保持可接受的速率失真性能的同时实现约61.04%的境内编码时间。 (c)2021个SPIE和IS&T [DOI:10.1117 / 1.JEI.30.2.023002]视频流量继续以巨大的速度增长。根据思科的研究,1个视频消耗将超过2022年的全球IP流量的80%。不成意,新兴应用程序,如360°和高动态范围视频,从视频消费者迅速获得了很大的关注,并进一步推动了令人愉快的可满贯性视频内容。可预见的未来也将证明超史定义素质和高帧率视频的优势。由于这种快速的进化,需要比当前标准更高的编码效率

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