4,137 research outputs found

    Objective assessment of region of interest-aware adaptive multimedia streaming quality

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    Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality

    Video streaming

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    Data compression techniques applied to high resolution high frame rate video technology

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    An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

    Data compression applied to HHVT

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    A task order was written by the High Resolution, High Frame Rate Video Technology (HHVT) project engineers to study data compression techniques that could be applied to the HHVT system. Specifically, the goals of the HHVT data compression study are to accomplish the following: (1) Determine the downlink capabilities of the Space Shuttle and Space Station Freedom to support HHVT data (i.e., determine the maximum data rates and link availability); (2) Determine current and projected capabilities of high speed storage media to support HHVT data by determining their maximum data acquisition/transmission rates and volumes; (3) Identify which experiment in the HHVT Users' Requirement data base need data compression, based on the experiments' imaging requirements; (4) Select the best data compression technique for each of these users by identifying a technique that provides compression but minimizes distortion; and (5) Investigate state-of-the-art technologies for possible implementation of selected data compression techniques. Data compression will be needed because of the high data rates and larger volumes of data that will result from the use of digitized video onboard the Space Shuttle and Space Station Freedom

    Low-latency compression of mocap data using learned spatial decorrelation transform

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    Due to the growing needs of human motion capture (mocap) in movie, video games, sports, etc., it is highly desired to compress mocap data for efficient storage and transmission. This paper presents two efficient frameworks for compressing human mocap data with low latency. The first framework processes the data in a frame-by-frame manner so that it is ideal for mocap data streaming and time critical applications. The second one is clip-based and provides a flexible tradeoff between latency and compression performance. Since mocap data exhibits some unique spatial characteristics, we propose a very effective transform, namely learned orthogonal transform (LOT), for reducing the spatial redundancy. The LOT problem is formulated as minimizing square error regularized by orthogonality and sparsity and solved via alternating iteration. We also adopt a predictive coding and temporal DCT for temporal decorrelation in the frame- and clip-based frameworks, respectively. Experimental results show that the proposed frameworks can produce higher compression performance at lower computational cost and latency than the state-of-the-art methods.Comment: 15 pages, 9 figure
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