将Pel抽取与部分失真消除相结合提高SAD能量效率

Ismael Seidel, André Beims Bräscher, José Luís Almada Güntzel
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引用次数: 10

摘要

视频编码(VC)中最耗能的步骤是块匹配算法(BMA),即使使用简单的相似性度量,如绝对差和(SAD)。此外,随着最先进的VC标准(H.264/AVC, HEVC和VP9)支持的分辨率不断提高,SAD必须尽可能节能,以延长便携式移动设备的电池寿命。在SAD计算中减少运算次数的两种著名技术是Pel Decimation和Partial Distortion Elimination (PDE)。前者提供的节能取决于所选择的抽取比率,并以编码效率为代价。对于后者,节省的能量在编码效率上没有成本,但取决于视频内容和搜索参数。在这项工作中,我们提出了两种可配置的SAD4×4架构:一种设计用于使用四种Pel Decimation比率(1:1,4:3,2:1或4:1)中的一种动态操作,另一种能够使用PDE和Pel Decimation。我们使用22个来自Common Test Conditions (CTC)的视频样本,使用4种不同的量化参数(QPs)编码,模拟了运动估计过程中的Pel Decimation和PDE行为。因此,该模拟是在5.82×1012 PDE SADs上进行的。Pel Decimation的影响以Bjøntegaard Delta (BD)-Rate表示,范围从3.16%(1:1)到21.94%(4:1)。此外,我们发现,通过单独使用PDE(即,不使用Pel Decimation),可以将计算一个SAD所需的周期数从10减少到6.38(平均)。为了展示在能量方面的改进,我们使用45nm标准细胞库合成了这两种结构。最后,使用PDE比单独使用Pel Decimation更能提高能源效率,而不会降低编码效率。
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Combining Pel Decimation with Partial Distortion Elimination to increase SAD energy efficiency
The most energy-hungry step of Video Coding (VC) is the Block Matching Algorithm (BMA), even when a simple similarity metric such as the Sum of Absolute Differences (SAD) is employed. Moreover, with the increasing resolutions supported by state-of-the-art VC standards (H.264/AVC, HEVC and VP9), the SAD must be as energy-efficient as possible to increase the battery lifetime in portable mobile devices. Two well-known techniques to decrease the number of operations in SAD calculation are Pel Decimation and Partial Distortion Elimination (PDE). The energy savings provided by the former are dictated by the chosen decimation ratio and comes with a cost in coding efficiency. For the latter, energy savings have no cost in coding efficiency but are dictated by the video content and search parameters. In this work we present two configurable SAD4×4 architectures: one designed to dynamically operate using one among four Pel Decimation ratios (1:1, 4:3, 2:1 or 4:1) and the other one able to use PDE in addition to Pel Decimation. We simulated Pel Decimation and PDE behavior during motion estimation using 22 video samples from the Common Test Conditions (CTC) encoded using 4 different quantization parameters (QPs). Thus, this simulation was performed over 5.82×1012 PDE SADs. The Pel Decimation impacts are shown in terms of Bjøntegaard Delta (BD)-Rate, ranging from 3.16% (1:1 ratio) up to 21.94% (4:1). In addition, we found that by using PDE solely (i.e., without Pel Decimation) one can reduce from 10 to 6.38 (in average) the number of required cycles to calculate one SAD. To show the improvements in terms of energy, we synthesized both presented architectures using a 45nm standard cell library. Finally, the use of PDE can improve energy efficiency more than Pel Decimation alone, without coding efficiency degradation.
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Adaptive energy minimization of embedded heterogeneous systems using regression-based learning Energy management via PI control for data parallel applications with throughput constraints Energy-efficient Level Shifter topology Asynchronous sub-threshold ultra-low power processor Combining Pel Decimation with Partial Distortion Elimination to increase SAD energy efficiency
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