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The new generation of the high-efficiency video coding (HEVC) video coding standard has improved compression performance and brought considerable coding complexity. Therefore, reducing the perception redundancy of video to obtain a better compression effect is the new direction of video development at present. In this paper, the HEVC is improved and enhanced from the aspects of a video saliency algorithm based on an attention mechanism and a video compression algorithm based on perception priority. In terms of video saliency, this paper proposes a spatial saliency algorithm based on a convolutional neural network and a temporal saliency algorithm based on motion vector. The saliency algorithm can combine the motion estimation results of each block during the HEVC compression on the basis of convolutional neural network and carry out adaptive dynamic fusion of the two, to complete the saliency map of the input video. In the aspect of the video compression algorithm with perception priority, this paper proposes a more flexible QP selection method, which selects its corresponding QP according to the saliency value of CU. At the same time, we propose a new rate-distortion optimization algorithm, which integrates the current block’s saliency feature into the traditional rate-distortion calculation method, to guide the allocation of bits and achieve the purpose of perception priority. The experimental results proved the superiority of the proposed method over the state-of-the-art perceptual coding algorithms in terms of saliency detection and perceptual compression quality. [ABSTRACT FROM AUTHOR]

Based on the dynamical theory of multi-body systems with nonholonomic constraints and an algorithm for complementarity problems, a numerical method for the multi-body systems with two-dimensional Coulomb dry friction and nonholonomic constraints is presented. In particular, a wheeled multi-body system is considered. Here, the state transition of stick-slip between wheel and ground is transformed into a nonlinear complementarity problem (NCP). An iterative algorithm for solving the NCP is then presented using an event-driven method. Dynamical equations of the multi-body system with holonomic and nonholonomic constraints are given using Routh equations and a constraint stabilization method. Finally, an example is used to test the proposed numerical method. The results show some dynamical behaviors of the wheeled multi-body system and its constraint stabilization effects. [ABSTRACT FROM AUTHOR]

Flood frequency analysis generally involves the use of simple parametric probability distributions to smooth and extrapolate the information provided by short flood records to estimate extreme flood flow quantiles. Parametric probability distributions can have difficulty simultaneously fitting both the largest and smallest floods. A danger is that the smallest observations in a record can distort the exceedance probabilities assigned to the large floods of interest. The identification and treatment of such Potentially Influential Low Floods (PILFs) frees a fitting algorithm to describe the distribution of the larger observations. This can allow parametric flood frequency analysis to be both efficient, and also robust to deviations from the proposed probability model's lower tail. Historically, PILF identification involved subjective judgement. We propose a new multiple Grubbs-Beck outlier test (MGBT) for objective PILF identification. MGBT PILF identification rates (akin to Type I errors) are reported for the lognormal (LN) distribution and the log-Pearson Type III (LP3) distribution with a variety of skew coefficients. MGBT PILF identification generally matched subjective identification from a recent California flood frequency study. Monte Carlo results show that censoring of PILFs identified by the MGBT algorithm improves the extreme quantile estimator efficiency of the expected moments algorithm (EMA) for negatively skewed LP3 distributions and has little effect for zero or positive skews; simultaneously it protects against deviations from the LP3 in the lower tail, as illustrated by distorted LN examples. Thus, MGBT generally makes flood frequency analysis based on the LP3 distribution with EMA both more accurate and more robust. [ABSTRACT FROM AUTHOR]