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It has been recognized that videos have to be encoded in a rate-distortion optimized manner for high coding performance. Therefore, operational coding methods have been developed for conventional distortion metrics such as Sum of Squared Error (SSE). Nowadays, with the rapid development of machine learning, the state-of-the-art learning based metric Video Multimethod Assessment Fusion (VMAF) has been proven to outperform conventional ones in terms of the correlation with human perception, and thus deserves integration into the coding framework. However, unlike conventional metrics, VMAF has no specific computational formulas and may be frequently updated by new training data, which invalidates the existing coding methods and makes it highly desired to develop a rate-distortion optimized method for VMAF. Moreover, VMAF is designed to operate at the frame level, which leads to further difficulties in its application to today’s block based coding. In this paper, we propose a VMAF oriented perceptual coding method based on piecewise metric coupling. Firstly, we explore the correlation between VMAF and SSE in the neighborhood of a benchmark distortion. Then a rate-distortion optimization model is formulated based on the correlation, and an optimized block based coding method is presented for VMAF. Experimental results show that 3.61% and 2.67% bit saving on average can be achieved for VMAF under the low_delay_p and the random_access_main configurations of HEVC coding respectively.

We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation using auto-encoder-based motion compression and a residual representation learned through motion refinement. This two-step encoding of human motion captures human motion in two stages: a general human motion estimation step that captures the coarse overall motion, and a residual estimation that adds back person-specific motion details. Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.

Venetian blinds are widely used to improve the performance of window systems, while their energy-saving potentials vary with different materials. This paper aims to undertake a standardized evaluation of the annual energy-saving potential of venetian blinds in residential buildings, and identify the most influential parameters for design optimization. To achieve this goal, nine parameters were chosen to establish venetian blinds models in WINDOW. Then, 9072 shading models were coupled with building models for annual energy consumption simulation in EnergyPlus, where the shading schedule was derived from questionnaires. Further, an experiment was conducted to verify the reliability of the joint simulation between WINDOW and EnergyPlus. Finally, annual energy performance (AEP) was adopted for a standardized energy efficiency evaluation of various venetian blinds products, and the sensitivity analysis of AEP to various parameters was conducted to identify the key parameters. The results indicated that by adjusting the key parameters of the venetian blinds, cooling energy saving can reach 44% and the heating energy saving can be up to 65%. The significant energy saving potential of venetian blinds indicates the prospect and importance of their use in residential buildings.

The MnBi permanent magnetic alloys with high coercivity was prepared by a low-energy ball milling process. The coercivity of the MnBi alloys can be controlled by tuning the particle size and the size distribution of the MnBi alloys powders through the milling process. The size-dependent coercivity of the particles was expressed by a function of Hc = 106.17−0.56·log(D), where the particle size (D) was supposed to be lager than the single-domain size. With an optimized treatment, a room-temperature coercivity of ∼1.9 T was achieved in the MnBi powders. The magnetization curves and micromagnetic analyses indicated that the magnetization process of the MnBi alloys was governed by the coherent magnetization rotation. The hardening mechanism was corresponding to the nucleation process in the anisotropic MnBi powders with refined particle size.

Vessel behaviour analysis plays an important role in maritime situational awareness. However, available technology still provides only limited approaches to vessel behaviour analysis. In this paper, we propose a visual analytics framework to interactively explore the characteristics of vessel behaviour by means of integrating visualisation with data mining and a human-computer interaction controlling model, which combines human insight with the enormous storage and processing capacities of computers to gain insight into vessel behaviour. In addition, we provide multiple views for visually analysing vessel trajectories, densities and speeds. Case studies with 15 days' AIS data collected from the middle Hankou channel to Yangluo channel in the Yangtze River demonstrate the effectiveness of our approach.

The low temperature phase (LTP) compound of MnBi is a promising, rare-earth metal free, and permanent magnetic material. However, it is difficult to obtain pure LTP MnBi, which results from a peritectic reaction between Mn and Bi atoms. In this study, the microstructural evolution and phase transformation kinetics of MnxBi100-x (x = 50, 55, 60) alloys were systematically investigated. Choosing an appropriate Mn content (Mn55Bi45) was found to promote the formation of the LTP MnBi. The results of the phase transformation kinetics indicated that the nucleation, growth and soft impingement processes are involved in the phase transformation process. In addition, in the initial stage of the LTP phase transformation, it was shown that the diffusional transformation was governed by two- and three-dimensional nucleation and growth, as well as a single mechanism of diffusion-controlled growth, which describes the entire transformation. At the final stage of the phase transformation, diffusion controlled growth, with soft impingement effects, dominates the process. The phase transformation kinetics analysis was confirmed with the X-ray diffraction and scanning electron microscopy results. This work implies that the local activation energy, Ec(α), and local Avrami exponent, n(α), can be applied to characterize the phase transformation behaviors of the LTP MnBi alloy.

In the light of low costs and automatic assessment, objective visual quality metrics enjoy many important applications such as perceptual coding. Recently multiple metrics obtain further improvement by means of machine learning. However, due to the absence of specific formulas, it's often hard to incorporate learning based metrics into video coding. In this paper, taking the state-of-the-art learning based metric VMAF for example, we propose a method of perceptual coding in an inferential manner for learning based metrics. The rate distortion optimization is adapted during coding as well. Experimental results show that compared with conventional methods, the proposed method can achieve obvious bitrate saving under HEVC coding.

To understand rural–urban differences in stressors, this study compared the cognitive and emotional language in geolocated Twitter posts in the United States against survey-reported county-level trends from the Gallup-Sharecare Well-Being Index. Mentions of stress on Twitter can predict population-level trends in stress in both rural (R2=31.6%) and urban (R2=26.7%) communities. While mentions of poor health are limited to only rural areas with low socioeconomic status, higher emotional expression is associated with higher stress across all rural communities. Controlling for socioeconomic status, urban communities reporting higher stress are also more likely to discuss relationships on Twitter. The findings contribute to an understanding of how language use on social media acts as a barometer of the social and cultural differences between regions. The data and stress topics developed in this paper are publicly available and can be accessed at https://osf.io/af8ce/ .

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud., Accepted to The 11th International Conference on Machine Vision (ICMV 2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser/

Being developed by Joint Video Exploration Team (JVET), Future Video Coding (FVC) aims at higher resolutions and higher compression performance than the state-of-the-art HEVC standard, undoubtedly at the cost of further computing increases. As an efficient computing platform, Graphics Processing Unit (GPU) is often used to accelerate encoding. But with the adoption of instruction set acceleration in the reference software of FVC, previous methods often become less efficient or even lead to a lower speed. In this paper, based on the comparative analysis of the time consumption between HEVC and FVC, we propose a GPU based acceleration method for the most computation-intensive step - frame interpolation of FVC, where frame caching strategy and a multi-stream mechanism is designed to make the best of GPU resources. Experimental results show that compared with the instruction set accelerated reference software of FVC, our method could achieve average 67.12% speed-up gains on the interpolation module and average 6.35% speed-up gains on overall encoding with exactly the same performance as before.

As an open-source and computationally efficient High Efficiency Video Coding (HEVC) encoder, x265 has been gaining increasing popularity in video applications. x265 provides numerous encoding parameters in view of flexibility. However, proper and efficient setting of parameters often becomes a great challenge in practice. In this paper, we deeply investigate the influence of x265 parameters based on the Slow preset and pick out important parameters in terms of efficiency and complexity. Then a Markov Chain Monte Carlo (MCMC) based algorithm is proposed for efficient parameter adaptation at the target encoding time. This paper shows that carefully selected low-complexity encoding configurations can achieve the coding efficiency comparable to that of high-complexity ones. Specifically, average 26.72% encoding time reduction can be achieved while maintaining similar Rate Distortion (RD) performance to x265 presets using the proposed algorithm.