An efficient biprediction decision scheme of high efficiency video coding (HEVC) is proposed for fast-encoding applications. For low-delay video applications, bidirectional prediction can be used to increase compression performance efficiently with previous reference frames. However, at the same time, the computational complexity of the HEVC encoder is significantly increased due to the additional biprediction search. Although a some research has attempted to reduce this complexity, whether the prediction is strongly related to both motion complexity and prediction modes in a coding unit has not yet been investigated. A method that avoids most compression-inefficient search points is proposed so that the computational complexity of the motion estimation process can be dramatically decreased. To determine if biprediction is critical, the proposed method exploits the stochastic correlation of the context of prediction units (PUs): the direction of a PU and the accuracy of a motion vector. Through experimental results, the proposed method showed that the time complexity of biprediction can be reduced to 30% on average, outperforming existing methods in view of encoding time, number of function calls, and memory access.
In universal media access (UMA) environment, because of the heterogeneous networks and terminals, flexible video adaptation, that is performed according to the network conditions and terminal capabilities as well as user preferences, is required to maximize consumer experience and ensure Quality of Service (QoS). MPEG-21 Digital Item Adaptation (DIA) support an interoperable framework for effective and efficient video adaptation. Among MPEG-21 DIA tools, utility function that describes the relations among the feasible adaptation operation, resource constraint, and utility plays the most important role in adaptation process because the optimal adaptation operation is decided among the feasible adaptation operations with given constraints. Therefore, in this paper, the overall concept of MEPG-21 DIA based adaptation framework and formulation of utility function are presented. In addition, the feasibility of the adaptation framework is presented by applying it to a few use cases for generating utility function and applications to specific adaptation scenarios involving nonscalable and scalable video.