The irregular surface topography has significant effects on seismic wave propagation, including introduction of free surface related multiples, strong scattering, and distortion of the shapes of seismic events. Because of the high efficiency, the finite-difference time-domain (FDTD) method is the most widely used numerical approach to solve three-dimensional (3D) seismic wave equations. However, FDTD at a uniform rectangular mesh could suffer from strong spurious diffractions, due to the stairgrid approximation to irregular surfaces. To resolve this problem, we immerse the irregular surfaces into fractional points under a uniform rectangular mesh and impose the free surface boundary condition along the normal direction of the surfaces. The implementation process of the free surface boundary condition in presence of irregular surfaces can be viewed a generalization of the traditional mirror image method designed for a planar free surface. The developed finite-difference (FD) scheme with the immersed boundary method (IBM), denoted as IBM-FD, needs to update the wavefields at a small number of ghost nodes, implicitly. We adopt Seidel iterations plus a third-degree Lagrange polynomial interpolation to realize this purpose. Compared with the FD scheme without IBM, the IBM-FD scheme only increases the computational cost slightly, due to the cost to determine the ghost nodes and update the wavefields at the ghost nodes. We present 3D scalar wavefield simulation examples with irregular surface topography to confirm the accuracy and stability of IBM-FD.

Advanced driver assistance systems should not only make the driving experience safer and more comfortable, it should also have a positive effect on driving behaviors. In this paper, an instructor-like assistance system for collision avoidance is developed and realized on an actual vehicle. The proposed system is activated only if the driver is not operating the vehicle properly when facing a collision risk. The vehicle control is shared by the driver and the assistance system. It is controlled by servomotors. In order to fulfill this requirement, a constraint satisfaction problem (CSP) is proposed and solved based on safe driving constraints and predictive vehicle states. Vehicle motion is predicted by a combination of a dynamics model and a potential field model that reflects the driver's risk feeling to an obstacle. Improved driving behavior is verified and evaluated quantitatively based on driving simulator data. By comparing the driving data before and after using the assistance system, it is found that distance is increased and speed is reduced when passing an obstacle. As a result, driving behavior becomes safer for collision avoidance due to the system's instruction. Furthermore, an experiment with an actual vehicle also demonstrates the practicability of the control system and shows the influence of different safe driving constraints.

The arbitrary ratio transcoding usually introduces coding block grid misalignment, which results in difficulties to utilize the decoding information in coding blocks of source videos during the encoding phase. To reduce the large computation load of HEVC downscale transcoding in such situation, we propose an efficient transcoding method that we refer the decoded coding unit (CU) partitioning to accelerate partition decision, which takes up the most complexity in the encoding phase as well as the whole transcoding. First, we predict CU depth in pixel level according to decoded partitioning of source videos. Then, we propose adaptive rules to determine CU partitioning of target videos based on the prediction, so that we can make early CU splitting or pruning decision without complex recursive search. Experiments demonstrate that the proposed method achieves about 74% time reduction on average with acceptable BD-rate increase in the encoding phase compared to the encoder in reference software HM13.0.

Parallelization is an efficient solution for addressing the increased computational complexity in High Efficiency Video Coding (HEVC). To improve the intra-frame parallelism, an adaptive parallel method is proposed based on an encoding complexity model for HEVC. First, by establishing the relationship between encoding complexity and Rate Distortion Optimization (RDO) process, the encoding complexity is measured by the merge skip modes and coding unit partition statistics. Then a greedy algorithm is proposed to partition each frame into several independent regions for parallelism, and the encoding complexity of each region is precisely controlled to achieve computational complexity balancing for better parallelism. Extensive experimental results show that the proposed method can achieve up to 3.34× speedup against wave-front parallel processing (WPP), and 1.19× speedup against tiles with acceptable encoding efficiency loss for low delay video encoding.

With a lot of enhanced coding tools introduced, High Efficiency Video Coding (HEVC) achieves significant improvement in coding efficiency at the cost of increased computational complexity. To efficiently reduce the encoding time of HEVC, a wavefront-based high parallel (WHP) solution integrating novel data-level and task-level methods is proposed in this paper. On data level, optimal single-instruction-multiple-data algorithms are designed for the enhanced coding tools, i.e., replacing the multiplication in motion compensation by add and shift operations with reduced instruction cycles, removing the transpose in transform via realignment of coefficients, and minimizing the memory access in sum of absolute difference/sum of squared differences calculation by fully reusing the registers. On task level, a novel inter-frame wavefront (IFW) method is developed by effectively decreasing the dependence of wavefront parallel processing (WPP). In addition, a coding tree block level parallelism analysis method is presented to prove the superior of IFW method compared with other HEVC representative parallel methods. Besides, a three-level thread management scheme is proposed to best exploit the parallelism of IFW method and achieve corresponding encoding speedup. Extensive experimental results show that, the overall WHP solution can bring up to $57.65\times $ , $65.55\times $ , and $88.17\times $ speedup for HEVC encoding of Wide Video Graphics Array, 720p and 1080p standard test sequences, while maintaining the same coding performance as with WPP. The proposed solution is also applied in several leading video companies in China, providing HEVC video service for more than 1.3 million users everyday.

In the above paper [ibid., vol. 16, no. 7, p. 1915, Nov. 2014], the sentence "has provided HEVC service to over 1500 million people in China via the Xunlei Kankan video client" should have appeared as "has provided HEVC services to over 150 million people in China via the Xunlei Kankan video client." Also. J. Sun should have been noted as the corresponding author.

High Efficiency Video Coding (HEVC) is the new generation video coding standard which achieves significant improvement in coding efficiency. Although HEVC is promising in many applications, the increased computational complexity is a serious problem, which makes parallelization necessary in HEVC encoding. To better understand the bottleneck of parallelization and improve the encoding speed, in this paper, we propose a Coding Tree Blocks (CTB) level parallelism analysis method as well as a novel Inter-Frame Wavefront (IFW) parallel encoding method. First, by establishing the relationship between parallelism and dependence, parallelism is precisely described by CTB-level dependence as a criterion to evaluate different parallel methods of HEVC. On this basis, by effectively decreasing the dependence based on Wavefront Parallel Processing (WPP), IFW method is developed. Finally, with the proposed parallelism analysis method, IFW is theoretically proved to be of higher parallelism compared with other HEVC representative parallel methods. Extensive experimental results show that, the proposed method and implementation can bring up to 17.81x, 14.34x and 24.40x speedup for HEVC encoding of WVGA, 720p and 1080p standard test sequences with the same ignorable coding performance degradation as WPP, thus showing a promising technology for future large-scale HEVC video application.

Although the emerging video coding standard High Efficiency Video Coding (HEVC) successfully doubles the compression efficiency of H.264/AVC, its growing computational complexity makes real-time decoding of high-definition HEVC videos a very challenging issue for the existing personal computers and mobile devices. In this paper, a systematical, efficient HEVC decoding solution on general processors is provided, consisting of structure-level, data-level, and task-level approaches. First, a redesigned overall structure of a HEVC decoder with data redundancy reduction mechanism is introduced, which cuts down basic data operation cost and achieves an average decoding speedup of 2.37 × compared to the HM 10.0 decoder. On this basis, novel single-instruction multiple-data (SIMD) algorithms such as low-complexity motion compensation, transpose-free transform, symmetric deblocking filter, and parallel-index sample adaptive offset are developed, which further parallelize the data operations of each decoding task and bring another 2.67 × decoding speedup. Finally, a frame-based task-level parallel framework is employed with a flexible entry scheme to efficiently support the simultaneous processing of multiple decoding tasks for different HEVC parallel strategies. The overall solution achieves decoding fps of 40-75 for 4k HEVC videos on the Intel i7-2600 3.4 GHz quad-core processor (4-thread decoding) and 35-55 for 720p videos on the ARM Cortex-A9 1.2 GHz duo-core processor (2-thread decoding). This proposal is the recommended cross-platform HEVC decoding solution of Intel, AMD, and Cisco, and has provided HEVC service to over 1500 million people in China via the Xunlei Kankan video client.