Haris Chaudhry, Mario Raffo-Jara, C. S. Cárdenas, Cristopher Villegas
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引用次数: 0
摘要
在高效视频编码标准(High Efficiency Video coding Standard, HEVC)中提出了新的编码工具和算法,为了实现更好的压缩效率,减少编码时间,使其阶段适合硬件实现,以及其他独立的改进,目前仍在HM参考软件上提出新的编码工具和算法。特别是在运动估计(ME)过程的初始阶段,引入了高级运动矢量预测(AMVP)和动态搜索范围(DSR)算法,以确定运动矢量预测器(MVP)和搜索范围(SR),这是运动估计(ME)后续步骤中需要的参数。然而,这些新工具的显著复杂性增加了开发硬件(HW)加速器的需求。此外,在视频压缩的硬件架构领域,一些作者提出了解决依赖问题(这对性能有害)的技术——在这种情况下,是在ME的子阶段之间。因此,本文提出了一种集成的、同步的、无依赖的硬件架构,用于ME过程的初始阶段——MV预测和SR计算。在中间FPGA (kintex7 xc7k70tfbv676-1)上的综合结果表明,集成架构可以在每秒72帧(4:2:2子采样)的情况下实现高达8K的吞吐量,同时使用最多7.04%的FPGA资源(在切片LUT上)。
A Dependency-Free Real-Time UHD Architecture for the Initial Stage of HEVC Motion Estimation
Novel coding tools and algorithms were proposed in the High Efficiency Video Coding Standard (HEVC), and are still being proposed over the HM reference software in order to achieve a better compression efficiency, decrease encoding time, make its stages suitable for hardware implementation, and other independent improvements. Particularly, for the initial stage of the motion estimation (ME) process, the Advanced Motion Vector Prediction (AMVP) and the Dynamic Search Range (DSR) algorithms were introduced in the field targeting the determination of the motion vector predictor (MVP), also used as the search center, and search range (SR), which are parameters needed in the subsequent steps of motion estimation (ME). However, the significant complexity of these new tools enhances the need to develop hardware (HW) accelerators. Furthermore, in the field of HW architectures for video compression, techniques that solve dependency problems (which are detrimental to performance) — in this case, between sub-stages of ME— were proposed by some authors. Thereupon, an integrated and synchronized dependency-free HW architecture for the initial stage of the ME process — regarding MV prediction and SR calculation— is proposed in this paper. Synthesis results on a middle ground FPGA (Kintex-7 xc7k70tfbv676-1) show that the integrated architecture can achieve a throughput up to 8K at 72 frames-per-second (4:2:2 subsampling) while using a maximum of 7.04% of the FPGA resources (on slice LUT’s).