Macroblock level rate and distortion estimation applied to the computation of the Lagrange multiplier in H.264 compression

摘要

The optimal value of Lagrange multiplier, a trade-off factor between the conveyed rate and distortion measured at the signal reconstruction has been a fundamental problem of rate distortion theory and video compression in particular.ududThe H.264 standard does not specify how to determine the optimal combination of the quantization parameter (QP) values and encoding choices (motion vectors, mode decision). So far, the encoding process is still subject to the static value of Lagrange multiplier, having an exponential dependence on QP as adopted by the scientific community. However, this static value cannot accommodate the diversity of video sequences. Determining its optimal value is still a challenge for current research.ududIn this thesis, we propose a novel algorithm that dynamically adapts the Lagrange multiplier to the video input by using the distribution of the transformed residuals at the macroblock level, expected to result in an improved compression performance in the rate-distortion space.ududWe apply several models to the transformed residuals (Laplace, Gaussian, generic probability density function) at the macroblock level to estimate the rate and distortion, and study how well they fit the actual values. We then analyze the benefits and drawbacks of a few simple models (Laplace and a mixture of Laplace and Gaussian) from the standpoint of acquired compression gain versus visual improvement in connection to the H.264 standard.ududRather than computing the Lagrange multiplier based on a model applied to the whole frame, as proposed in the state-of-the-art, we compute it based on models applied at the macroblock level. The new algorithm estimates, from the macroblock’s transformed residuals, its rate and distortion and then combines the contribution of each to compute the frame’s Lagrange multiplier.ududThe experiments on various types of videos showed that the distortion calculated at the macroblock level approaches the real one delivered by the reference software for most sequences tested, although a reliable rate model is still lacking especially at low bit rate. Nevertheless, the results obtained from compressing various video sequences show that the proposed method performs significantly better than the H.264 Joint Model and is slightly better than state-of-the-art methods.