Motion vector composition through Lagrangian optimization for arbitrary frame-size video transcoding

Video transcoding is usually conducted when the device does not support the current format or has limited storage capacity. Video transcoding is a computation-intensive process that changes one format to another one, and various multimedia applications have made it important in recent years. We present a new motion vector (MV) composition algorithm for arbitrary frame-size video transcoding. The proposed method uses the relation between the prediction error and the required bits when encoding MVs to form an auxiliary function called the Lagrange function. Therefore, MV composition is converted into a constrained optimization problem. Through the Lagrangian optimization, a dominant MV is selected from a set of candidate MVs by minimizing this cost function. The major contribution of the proposed method is that we emphasize the effect of the bits required to encode MVs; therefore, at the same target bitrate, the proposed method provides better coding performance. Experimental results show that the proposed method has better performance in terms of both objective and subjective qualities than other existing methods.