Currently three up-to-date Terrestrial Reference Frames(TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station position...Currently three up-to-date Terrestrial Reference Frames(TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station positions and Earth orientation parameters, but the concept of combining these data is fundamentally different. The IGN approach is based on the combination of technique solutions, while the DGFI is combining the normal equation systems. Both yield in reference epoch coordinates and velocities for a global set of stations. JPL uses a Kalman filter approach, realizing a TRF through weekly time series of geocentric coordinates. As the determination of the CRF is not independent of the TRF and vice versa, the choice of the TRF might impact on the CRF, Within this work we assess this effect.We find that the estimated Earth orientation parameter(EOP) from DTRF2014 agree best with those from ITRF2014, the EOP resulting from JTRF2014 show besides clear yearly signals also some artifacts linked to certain stations. The estimated source position time series however, agree with each other better than ±1 μas. When fixing EOP and station positions we can see the maximal effect of the TRF on the CRF. Here large systematics in position as well as proper motion arise. In case of ITRF2008 they can be linked to the missing data after 2008. By allowing the EOP and stations to participate in the adjustment,the agreement increases, however, systematics remain.展开更多
首先分析了JMVC的参考预测结构,利用1个Go P(Group of Picture)中不同时间层上的帧与其参考帧之间的相关度不同和不同时间层上的帧数目也不同的特点,提出了去除奇数视点最高和次高时间层的视点间预测,并增加偶数视点非关键帧的最低和次...首先分析了JMVC的参考预测结构,利用1个Go P(Group of Picture)中不同时间层上的帧与其参考帧之间的相关度不同和不同时间层上的帧数目也不同的特点,提出了去除奇数视点最高和次高时间层的视点间预测,并增加偶数视点非关键帧的最低和次低时间层的视点间预测的预测结构。实验结果表明,提出的预测结构基本不影响视频质量,且表现出了更加出色的编码效率,编码的复杂度减少幅度高达15%,输出码率也有所减少,较好地改善了编码的实时性能。展开更多
基金supported by the Deutsche Forschungsgemeinschaft(DFG), Project Nr.:HE 5937/2-1 and NO318/ 13-1supported by the European Research Council(ERC) under the ERC-2017-STG SENTIFLEX project(Grant Agreement 755617)
文摘Currently three up-to-date Terrestrial Reference Frames(TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station positions and Earth orientation parameters, but the concept of combining these data is fundamentally different. The IGN approach is based on the combination of technique solutions, while the DGFI is combining the normal equation systems. Both yield in reference epoch coordinates and velocities for a global set of stations. JPL uses a Kalman filter approach, realizing a TRF through weekly time series of geocentric coordinates. As the determination of the CRF is not independent of the TRF and vice versa, the choice of the TRF might impact on the CRF, Within this work we assess this effect.We find that the estimated Earth orientation parameter(EOP) from DTRF2014 agree best with those from ITRF2014, the EOP resulting from JTRF2014 show besides clear yearly signals also some artifacts linked to certain stations. The estimated source position time series however, agree with each other better than ±1 μas. When fixing EOP and station positions we can see the maximal effect of the TRF on the CRF. Here large systematics in position as well as proper motion arise. In case of ITRF2008 they can be linked to the missing data after 2008. By allowing the EOP and stations to participate in the adjustment,the agreement increases, however, systematics remain.
文摘首先分析了JMVC的参考预测结构,利用1个Go P(Group of Picture)中不同时间层上的帧与其参考帧之间的相关度不同和不同时间层上的帧数目也不同的特点,提出了去除奇数视点最高和次高时间层的视点间预测,并增加偶数视点非关键帧的最低和次低时间层的视点间预测的预测结构。实验结果表明,提出的预测结构基本不影响视频质量,且表现出了更加出色的编码效率,编码的复杂度减少幅度高达15%,输出码率也有所减少,较好地改善了编码的实时性能。
基金supported by the Chinese National Natural Science Foundation(Nos.10878022,10903022,10903030)the Knowledge Innovation Project of CAS(KJCX2-yW-T13)+1 种基金the State Agency on Science,Innovation and Information of the Ministry for Education and Science of Ukrainethe Russian Foundation of Basic Research~~
