Complexity reduction of H.264 using Lagrange optimization methods

A complexity reduction algorithm for an H.264 encoder is proposed. Computational savings are achieved by identifying, prior to motion estimation, macroblocks that are likely to be skipped and hence saving further computational processing of these macroblocks. This early prediction is made by estimating a Lagrangian rate-distortion cost function which incorporates an adaptive model for the Lagrange multiplier parameter based on local sequence statistics. Simulation results demonstrate that the algorithm can achieve computational savings of 19-65% (depending on the source sequence) with no significant loss of rate-distortion performance.