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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者单位:Middle East Technical University
学位级别:博士
导师姓名:Prof. Dr. A. Aydin Alatan
授予年度:2012年
主 题:Video Adaptation Decoding Complexity Video Content Characteristics Quality of Experience
摘 要:Subjective video quality and video decoding complexity are jointly optimized in order to determine the video encoding parameters that will result in the best Quality of Experience (QoE) for an end user watching a video clip on a mobile device. Subjective video quality is estimated by an objective criteria, video quality metric (VQM), and a method for predicting the video quality of a test sequence from the available training sequences with similar content characteristics is presented. Standardized spatial index and temporal index metrics are utilized in order to measure content similarity. A statistical approach for modeling decoding complexity on a hardware platform using content features extracted from video clips is presented. The overall decoding complexity is modeled as the sum of component complexities that are associated with the computation intensive code blocks present in state-of-the-art hybrid video decoders. The content features and decoding complexities are modeled as random parameters and their joint probability density function is predicted as Gaussian Mixture Models (GMM). These GMMs are obtained off-line using a large training set comprised of video clips. Subsequently, decoding complexity of a new video clip is estimated by using the available GMM and the content features extracted in real time. A novel method to determine the video decoding capacity of mobile terminals by using a set of subjective decodability experiments that are performed once for each device is also proposed. Finally, the estimated video quality of a content and the decoding capacity of a device are combined in a utility-complexity framework that optimizes complexity-quality trade- offto determine video coding parameters that result in highest video quality without exceeding the hardware capabilities of a client device. The simulation results indicate that this approach is capable of predicting the user viewing satisfaction on a mobile device.