HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing net...HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing network channels,but encoding videos into multiple representations with selected encoding parameters is a significant challenge.However,video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds.In turn,the public clouds,such as Amazon elastic compute cloud(EC2),provide hundreds of computing instances optimized for different purposes and clients’budgets.Thus,there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations.Additionally,the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content.In this paper,we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple×264 codec encoding parameters and video sequences of varying complexity.Then,we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs.Furthermore,we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost.The results show that our approach,on average,can reduce the encoding costs by at least 15.8%and up to 47.8%when compared to a random selection of EC2 spot instances.展开更多
To aim at higher coding efficiency for multiview video coding, the multiview video with a modified high efficiency video coding（MV-HEVC）codec is proposed to encode the dependent views.However, the computational comp...To aim at higher coding efficiency for multiview video coding, the multiview video with a modified high efficiency video coding（MV-HEVC）codec is proposed to encode the dependent views.However, the computational complexity of MV-HEVC encoder is also increased significantly since MV-HEVC inherits all computational complexity of HEVC. This paper presents an efficient algorithm for reducing the high computational complexity of MV-HEVC by fast deciding the coding unit during the encoding process. In our proposal, the depth information of the largest coding units（LCUs） from independent view and neighboring LCUs is analyzed first. Afterwards, the analyzed results are used to early determine the depth for dependent view and thus achieve computational complexity reduction. Furthermore, a prediction unit（PU） decision strategy is also proposed to maintain the video quality. Experimental results demonstrate that our algorithm can achieve 57% time saving on average,while maintaining good video quality and bit-rate performance compared with HTM8.0.展开更多
基金This work has been supported in part by the Austrian Research Promotion Agency(FFG)under the APOLLO and Karnten Fog project.
文摘HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing network channels,but encoding videos into multiple representations with selected encoding parameters is a significant challenge.However,video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds.In turn,the public clouds,such as Amazon elastic compute cloud(EC2),provide hundreds of computing instances optimized for different purposes and clients’budgets.Thus,there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations.Additionally,the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content.In this paper,we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple×264 codec encoding parameters and video sequences of varying complexity.Then,we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs.Furthermore,we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost.The results show that our approach,on average,can reduce the encoding costs by at least 15.8%and up to 47.8%when compared to a random selection of EC2 spot instances.
基金supported by NSC under Grant No.NSC 100-2628-E-259-002-MY3
文摘To aim at higher coding efficiency for multiview video coding, the multiview video with a modified high efficiency video coding（MV-HEVC）codec is proposed to encode the dependent views.However, the computational complexity of MV-HEVC encoder is also increased significantly since MV-HEVC inherits all computational complexity of HEVC. This paper presents an efficient algorithm for reducing the high computational complexity of MV-HEVC by fast deciding the coding unit during the encoding process. In our proposal, the depth information of the largest coding units（LCUs） from independent view and neighboring LCUs is analyzed first. Afterwards, the analyzed results are used to early determine the depth for dependent view and thus achieve computational complexity reduction. Furthermore, a prediction unit（PU） decision strategy is also proposed to maintain the video quality. Experimental results demonstrate that our algorithm can achieve 57% time saving on average,while maintaining good video quality and bit-rate performance compared with HTM8.0.
