Rule of Thirds Based Fast Color Error Evaluation for QoE- Guaranteed Mobile Media Services

Video streaming has become one of the most essential ways in online communication. While video streaming is widely used in most of the Mobile Multimedia applications, it is not only necessarily required to maintain the high video quality but also minimized the video processing time to ensure the Quality of Service (QoS) as well as Quality of Experience (QoE) of the system in live broadcasting. This paper introduces the different way to evaluate the transmission errors in video applications by measuring the error with un-uniform distribution of information importance factor in pixels to achieve a suitable order in error correction. By changing the QoS evaluating method by considering Regions of Interest, especially fundamental rules in art study such Rule of Thirds, QoE should be improved to get closer to the viewer concerns and increase the system efficiency. Error detection as well as correction should be significantly corrected in priority orders

3D motion matching algorithm using signature feature descriptor

This paper introduces a basic frame for rehabilitation motion practice system which detects 3D motion trajectory with the Microsoft Kinect (MSK) sensor system and proposes a cost-effective 3D motion matching algorithm. The rehabilitation motion practice system displays a reference 3D motion in the database system that the player (patient) tries to follow. The player’s motion is traced by the MSK sensor system and then compared with the reference motion to evaluate how well the player follows the reference motion. In this system, 3D motion matching algorithm is a key feature for accurate evaluation for player’s performance. Even though similarity measurement of 3D trajectories is one of the most important tasks in 3D motion analysis, existing methods are still limited. Recent researches focus on the full length 3D trajectory data set. However, it is not true that every point on the trajectory plays the same role and has the same meaning. In this situation, we developed a new cost-effective method that only uses the less number of features called ‘signature’ which is a flexible descriptor computed from the region of ‘elbow points’. Therefore, our proposed method runs faster than other methods which use the full length trajectory information. The similarity of trajectories is measured based on the signature using an alignment method such as dynamic time warping (DTW), continuous dynamic time warping (CDTW) or longest common sub-sequence (LCSS) method. In the experimental studies, we applied the MSK sensor system to detect, trace and match the 3D motion of human body. This application was assumed as a system for guiding a rehabilitation practice which can evaluate how well the motion practice was performed based on comparison of the patient’s practice motion traced by the MSK system with the pre-defined reference motion in a database. In order to evaluate the accuracy of our 3D motion matching algorithm, we compared our method with two other methods using Australian sign word dataset. As a result, our matching algorithm outperforms in matching 3D motion, and it can be exploited for a base framework for various 3D motion-based applications at low cost with high accuracy

Development of an EPIC parallel computing framework to facilitate regional/global gridded crop modeling with multiple scenarios: A case study of the United States

Crop models are increasingly used to evaluate crop yields at regional/global scales. These applications require the integration and processing of very large data sets in order to explore the implications of land management options across spatially heterogeneous scales. These modeling involve the combination of large spatially explicit data sets for climate, biophysical and crop management variables as well as significant computational capacity for regional/global scale simulations. As a result, the application of crop models at regional/global scales is challenging due to the requirements for input data, calibration, validation and simulation setups appropriate for thousands to millions of spatial points. Not surprisingly, the implementation of these models across large areas using fine-scale grids can be limited by computational time requirements. To reduce the large computational load of an agroecosystem simulation process for regional and global scales, we developed an EPIC Parallel Computing Framework (EPCF) to facilitate regional/global gridded crop modeling. The EPCF can make full use of the CPU resources of the workstation through parallel processing. For future users, only a few lines of additional code modification are needed to convert the single process code to parallel computing code. Parallel processing in one machine makes it easy to handle the whole system without the overhead and expertise required for a distributed system. EPCF is a system that provides not only the ease of development but also cost efficiency