A Unified Approach for Online Video Quality Control using Deep Neural Network Technique


A Unified Approach for Online Video Quality Control using Deep Neural Network Technique – We propose a novel framework for automatic video quality control, which includes an active learning setting, which can be used to learn the properties of the video by predicting the video quality parameters for each individual instance. This framework is built upon a novel training scenario where the training data is generated by an agent and the control system learns to optimize the video quality parameters, using neural networks. We demonstrate that our framework leads to effective learning of video to improve the quality of the video. We discuss various methods and show how our framework can be used as a generic framework for video quality control and an efficient user-friendly software.

Theano: a powerful compressive n-gram generator is a natural extension of Theano to the domain of language. In this paper, We present and evaluate a novel approach for learning a new language by exploiting a variety of techniques of the Theano, including the use of the N-gram. This approach is also motivated on the grounds that we can learn a language using an N-gram generator without any training data and with no knowledge of the N-gram generator’s vocabulary size. We develop and evaluate this approach using synthetic language and syntactic resources from various scientific institutions and demonstrate how it improves performance over Theano, on both synthetic and real N-gram generation.

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A Unified Approach for Online Video Quality Control using Deep Neural Network Technique

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  • Dynamic Network Models: Minimax Optimal Learning in the Presence of Multiple Generators

    Theano: a powerful compressive n-gram generatorTheano: a powerful compressive n-gram generator is a natural extension of Theano to the domain of language. In this paper, We present and evaluate a novel approach for learning a new language by exploiting a variety of techniques of the Theano, including the use of the N-gram. This approach is also motivated on the grounds that we can learn a language using an N-gram generator without any training data and with no knowledge of the N-gram generator’s vocabulary size. We develop and evaluate this approach using synthetic language and syntactic resources from various scientific institutions and demonstrate how it improves performance over Theano, on both synthetic and real N-gram generation.


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