ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (11): 2469-2484.

### Elastic Motion Estimation Algorithm Using Two-Bit-Depth Pixels

Song Chuanming1,2,4, Min Xin1,3, Xie Weidong1,3, Yin Baocai2, Wang Xianghai1

1. 1(School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning 116029);2(School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024);3(School of Computer Science and Engineering, Northeastern University, Shenyang 110169);4(State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023)
• Online:2019-11-12

Abstract: To reduce the computational complexity of traditional elastic motion estimation, this paper proposes a novel elastic motion estimation algorithm using two-bit-depth pixels. First, the Prewitt operator is employed to calculate the gradient of each video frame. The mean and standard deviation of the gradient norm is utilized to down-sample each pixels depth from 8 b into 2 b. Second, the bitwise operation-based matrix multiplication and the comparison based partial derivative computation are introduced. We subsequently describe an elastic motion model using two-bit-depth pixels, as well as a simplified Gaussian-Newton method which avoids the repetitive computation of the Hessian matrix and its inverse matrix. Meanwhile, we establish the functional relationship of the damping step size versus motion vector increment and motion-compensated errors by the first-order linear approximation, obtaining a method for approximately calculating the initial step size. Furthermore, we address a fast method for solving the elastic motion model with two-bit-depth pixels, using the diamond search algorithm as initial search. Experimental results illustrate that our algorithm obviously outperforms the full search with eight-bit-depth pixels, the full search with two-bit-depth pixels, as well as the conventional elastic motion estimation with eight-bit-depth pixels in terms of the peak signal-to-noise ratio (PSNR) and computational efficiency.

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