Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Heng Chen, Xiaofeng Huang, Zehao Tao, Qinghua Sheng, Yan Cui, Yang Zhou, Haibing Yin
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引用次数: 0

Abstract

To enhance compression efficiency, the AV1 video coding standard has introduced several new intra-prediction modes, such as smooth and finer directional prediction modes. However, this addition increases computational complexity and hinders parallelized hardware implementation. In this paper, a hardware-friendly rough mode decision (RMD) algorithm and its fully pipelined hardware architecture design are proposed to address these challenges. For algorithm optimization, firstly, a novel directional mode pruning algorithm is proposed. Then, the sum of absolute transform differences (SATD) cost accumulated approximation method is adopted during the tree search. Finally, in the reconstruction stage, a reconstruction approximation model based on the DC transform is proposed to solve the low-parallelism problem. For hardware architecture design, the proposed fully pipelined hardware architecture is implemented with 28 pipeline stages. This design can process multiple prediction modes in parallel. Experimental results show that the proposed fast algorithm achieves 46.8% time savings by 1.96% Bjøntegaard delta rate (BD-Rate) increase on average under all-intra (AI) configuration. When synthesized under the 28nm UMC technology, the proposed hardware can operate at a frequency of 316.2 MHz with 1113.14 K gate count.

Abstract Image

AV1 编码器的快速粗略模式决策算法和硬件架构设计
为了提高压缩效率,AV1 视频编码标准引入了几种新的内部预测模式,如平滑和更精细的定向预测模式。然而,这种新增模式增加了计算复杂度,阻碍了硬件的并行化实施。本文提出了一种硬件友好型粗略模式决策(RMD)算法及其全流水线硬件架构设计,以应对这些挑战。在算法优化方面,首先提出了一种新颖的定向模式剪枝算法。然后,在树搜索过程中采用了绝对变换差值之和(SATD)成本累积近似法。最后,在重构阶段,提出了一种基于直流变换的重构近似模型,以解决低并行性问题。在硬件架构设计方面,提出的全流水线硬件架构有 28 个流水线级。这种设计可以并行处理多种预测模式。实验结果表明,在全内核(AI)配置下,所提出的快速算法平均提高了 1.96% 的比恩特加德三角率(BD-Rate),节省了 46.8% 的时间。在 28nm UMC 技术下合成时,所提出的硬件可以在 316.2 MHz 频率下运行,门数为 1113.14 K。
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来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
审稿时长
6 months
期刊介绍: Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
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