Machine learning based video coding optimizations: A survey

Zhang, Yun; Kwong, Sam; Wang, Shiqi

doi:10.1016/j.ins.2019.07.096

Cited by 68 publications

(26 citation statements)

References 89 publications

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“…As shown in Figure 10(a), the prediction of air temperature and humidity offered the least RMSE when the window size was around 20. Meanwhile, the prediction of CO 2 and NH 3 had the least RMSE when the window size was within the range [15,20]. By contrast, the least RMSE appeared when the window size was 10 for H 2 S. Hence, the aforementioned window sizes basically matched the result calculated in formula (3).…”

Section: Anomaly Data Detectionmentioning

confidence: 59%

“…Deep learning allows computational models consisting of multiple hierarchical layers to learn fantastically complex, subtle, and abstract representations instead of traditional handcrafted local descriptors and discriminative classifiers. [19][20][21][22] In the past several years, deep learning methods have been increasingly attracting research attention in many computer vision application areas. It has been shown that these techniques have provided significant improvement for object detection, including animal behavior.…”

Section: Introductionmentioning

confidence: 99%

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On-farm welfare monitoring system for goats based on Internet of Things and machine learning

Rao

Jiang

Wang

et al. 2020

International Journal of Distributed Sensor Networks

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Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

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Section: Anomaly Data Detectionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

On-farm welfare monitoring system for goats based on Internet of Things and machine learning

Rao

Jiang

Wang

et al. 2020

International Journal of Distributed Sensor Networks

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“…The face resolutions in these images range from 1×2 to 335×296. The resolution of most faces in our dataset is below 300 pixel 2 and the the face number mostly falls into the range [1,20].…”

Section: B (Semi-)supervised Face Detection In the Low Light Conditionmentioning

confidence: 99%

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Yang

Yuan

Ren

et al. 2020

IEEE Trans. on Image Process.

192

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Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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“…Specifically targeting on video transcoding, previous works such as [28], [29], [30], have modeled the output and complexity of an HEVC encoder as a function of a few encoding parameters by exhaustive application profiling, considering a few encoding parameters due to the exponential complexity of dealing with a higher number. But these models are considerably platform-dependent and any change in the target architecture may result in an intolerable model error.…”

Section: Related Workmentioning

confidence: 99%

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

Costero

Iranfar

Zapater

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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The advent of online video streaming applications and services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher video quality and more compression at the cost of increased complexity. On one hand, HEVC exposes a set of dynamically tunable parameters to provide trade-offs among Quality-of-Service (QoS), performance, and power consumption of multi-core servers on the video providers' data center. On the other hand, resource management of modern multi-core servers is in charge of adapting system-level parameters, such as operating frequency and multithreading, to deal with concurrent applications and their requirements. Therefore, efficient multiuser HEVC streaming necessitates joint adaptation of application-and system-level parameters. Nonetheless, dealing with such a large and dynamic design space is challenging and difficult to address through conventional resource management strategies. Thus, in this work, we develop a multi-agent Reinforcement Learning framework to jointly adjust application-and system-level parameters at runtime to satisfy the QoS of multiuser HEVC streaming in power-constrained servers. In particular, the design space, composed of all design parameters, is split into smaller independent sub-spaces. Each design sub-space is assigned to a particular agent so that it can explore it faster, yet accurately. The benefits of our approach are revealed in terms of adaptability and quality (with up to to 4× improvements in terms of QoS when compared to a static resource management scheme), and learning time (6× faster than an equivalent mono-agent implementation). Finally, we show that the power-capping techniques formulated outperform the hardware-based power capping with respect to quality.

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Machine learning based video coding optimizations: A survey

Cited by 68 publications

References 89 publications

On-farm welfare monitoring system for goats based on Internet of Things and machine learning

On-farm welfare monitoring system for goats based on Internet of Things and machine learning

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

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