Towards Model-based Transrating of H.264 coded video

A common approach for video transrating (bit rate reduction) is to requantize the transform coefficients. Optimal requantization aims to find a set of new step-sizes that achieve the target bit rate while introducing minimal distortion. Since the state of the art H.264 standard coder constrains requantization by limiting the amount of change in the quantization step-size from one macroblock to the next, the common Lagrangian optimization approach cannot be applied. We propose a solution to this dependency problem by extending each Lagrangian iteration with a constrained dynamic programming stage. Further, in order to reduce the computational load of evaluating the rate and distortion at each macroblock for multiple step-sizes, we suggest analytic models that can be applied for this purpose. The developed models are suitable for requantization and are matched to the context-adaptive entropy coding used in H.264. The proposed algorithm performs the requantization in the compressed domain and currently supports inter coded frames only. It reduces the run-time by a factor of 4, as compared to the full exhaustive optimization, and achieves up to 1[dB] gain in PSNR, as compared to a simple one-pass algorithm.