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• The focal spot with accurate position can be straightforwardly realized. • Apply two non-iterative methods to achieve multifocal spots with accurate position. • The complex amplitude coding method produced multifocal spots with higher uniformity and stability than the fan-shaped subzone phase method. • Three-dimensional particle manipulation dynamic optical trapping experiment. Holographic optical trapping enables three-dimensional (3D) particle manipulation based on a traditional iterative algorithm. However, this type of particle manipulation is typically unstable. It is challenging to realize continuous dynamic 3D optical trapping because a unique solution of uniformity cannot be obtained over several time-consuming iterations. In this study, 3D multifocal spots with high uniformity were generated via non-iterative algorithms (using a complex amplitude coding method and fan-shaped subzone phase method) to realize stable dynamic optical trapping for 3D particle manipulation. The corresponding experimental results pave the way for potential applications of colloid transport and the micro-assembly of objects in the micro-operation field. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC) improves the compression efficiency at the cost of high computational complexity by using the quad-tree coding unit (CU) structure and variable prediction unit (PU) modes. To minimize the HEVC encoding complexity while maintaining its compression efficiency, a binary and multi-class support vector machine (SVM)-based fast HEVC encoding algorithm is presented in this paper. First, the processes of recursive CU decision and PU selection in HEVC are modeled as hierarchical binary classification and multi-class classification structures. Second, according to the two classification structures, the CU decision and PU selection are optimized by binary and multi-class SVM, i.e., the CU and PU can be predicted directly via classifiers without intensive rate distortion (RD) cost calculation. In particular, to achieve better prediction performance, a learning method is proposed to combine the off-line machine learning (ML) mode and on-line ML mode for classifiers based on a multiple reviewers system. Additionally, the optimal parameters determination scheme is adopted for flexible complexity allocation under a given RD constraint. Experimental results show that the proposed method can achieve 68.3%, 67.3%, and 65.6% time saving on average while the values of Bjøntegaard delta peak signal-to-noise ratio are −0.093dB, −0.091dB, and −0.094dB and the values of Bjøntegaard delta bit rate are 4.191%, 3.842%, and 3.665% under low delay $P$ main, low delay main, and random access configurations, respectively, when compared with the HEVC test model version HM 16.5. Meanwhile, the proposed method outperforms the state-of-the-art fast coding algorithms in terms of complexity reduction and RD performance. [ABSTRACT FROM PUBLISHER]

To improve the coding efficiency and meanwhile reduce the complexity of the depth video encoder in 3D system, we present an efficient depth coding scheme based on the allowable depth distortion (ADD) in view synthesis. Firstly, we analyze the depth distortion in view synthesis and present a new ADD-based Rate Distortion (RD) optimization cost function to improve the mode/reference decision criteria. Then, we present an early mode and reference frame selection algorithm, which skips the unnecessary and complex mode and reference frame when the depth distortion is within the ADD interval. Meanwhile, the coding efficiency is improved by RD optimized mode decision and reference frame selection. The experimental results show that the proposed overall algorithm, which integrates the fast mode decision, reference frame selection and RD optimization, can reduce the depth coding complexity from 32.47 to 72.59, and 55.95 % on average over different test sequences. Meanwhile, the proposed algorithm achieves 1.34 and 1.58 dB average Bjontegaard Delta peak signal-to-noise ratio gain in terms of the view synthesis quality at low and high bit rate, respectively. In terms of the bit rate reduction, it reduces 54.66 and 49.60 % Bjontegaard Delta bit rate on average at low and high bit rate, respectively, while maintaining the same view synthesis image quality. [ABSTRACT FROM AUTHOR]

Highlights • We model the RD cost comparison as a MDP for low complexity video coding. • We learn a CU early termination classifier with end-to-end actor-critic RL algorithm. • A CU early termination algorithm independent of depths is derived for whole CTU. Abstract In this paper, we propose a Reinforcement Learning (RL) based Coding Unit (CU) early termination algorithm for High Efficiency Video Coding (HEVC). RL is utilized to learn a CU early termination classifier independent of depths for low complexity video coding. Firstly, we model the process of CU decision as a Markov Decision Process (MDP) according to the Markov property of CU decision. Secondly, based on the MDP, a CU early termination classifier independent of depths is learned from trajectories of CU decision across different depths with the end-to-end actor-critic RL algorithm. Finally, a CU decision early termination algorithm is introduced with the learned classifier, so as to reduce computational complexity of CU decision. We implement the proposed scheme with different neural network structures. Two different neural network structures are utilized in the implementation of RL based video encoder, which are evaluated to reduce video coding complexity by 34.34 % and 43.33 %. With regard to Bj ø ntegaard delta peak signal-to-noise ratio and Bj ø ntegaard delta bit rate, the results are −0.033 dB and 0.85 % , −0.099 dB and 2.56 % respectively on average under low delay B main configuration, when compared with the HEVC test model version 16.5. [ABSTRACT FROM AUTHOR]