Journal of Optoelectronics · Laser, Volume. 33, Issue 2, 217(2022)
Reconfigurable design and implementation of advanced residual prediction algorithm based on DPRAP
In order to solve problems of long encoding time and high hardware acceleration resource occupancy rate caused by the advanced residual prediction (ARP) algorithm which does not make full use of the data characteristics of depth map in three-dimensional high efficiency video coding (3D-HEVC),a fast ARP selection algorithm based on reconfigurable hardware implementation is proposed.Firstly,the depth map is divided into three regions according to its data characteristics,and then the threshold is set to select the advanced residual prediction algorithm in different regions quickly,so as to reduce the coding time.The experimental results show that compared with the standard platform HTM16.1,the fast selection algorithm reduces the encoding time by 8.10% when the average peak signal to noise ratio 〖WTBX〗(PSNR) loss is only 0.019 dB.Finally,the dynamic programmable reconfigurable array processor (DPRAP) is used to accelerate the ARP fast selection algorithm in parallel,and then a reconfigurable implementation scheme is proposed based on the reconfiguration mechanism of the array processor,so as to accelerate the algorithm and reduce the hardware resource occupancy.The experimental results show that compared with the parallel scheme,the total number of the process element (PE) and instructions are respectively reduced by 50% and 33.23%,and the average speedup is 1.9.The algorithm before and after optimization is compared with disparity estimation,and the average speedup is 2.5.Therefore,this study has a certain reference value for real-time video coding of 3D-HEVC algorithm.
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XIE Xiaoyan, WANG Shuxin, ZHU Yun, ZHANG Xihong, JI Shentao. Reconfigurable design and implementation of advanced residual prediction algorithm based on DPRAP[J]. Journal of Optoelectronics · Laser, 2022, 33(2): 217
Received: May. 14, 2021
Accepted: --
Published Online: Oct. 9, 2024
The Author Email: XIE Xiaoyan (xxy@xupt.edu.cn)