On model parameter estimation for H.264/AVC rate control

We present an idea of improving the rate quantization (R-Q) model based rate control for H.264/AVC from a novel model parameter estimation perspective. Our idea is different from the conventional belief that emphasizes the statistical accuracy property of an R-Q model. Instead, in our opinion, a good R-Q model should also balance the model accuracy with dynamic properties such as the predictability of its model parameter. This new angle is important for two primary reasons: First, due to the non-stationary nature of video signals, deviation from the assumptions for an R-Q model is almost inevitable. Second, for timing-sensitive applications, model parameters are not known in advance and thus have to be estimated on the fly where estimation errors cannot be avoided. We found that compared to the quadratic R-Q model [5], while the linear R-Q model is a bit less accurate when the statistical data is known, its model parameter has much better predictability and faster to estimate. Taking advantage of these nice properties of the linear R-Q model, the rate control for H.264/AVC yields significantly better rate distortion performance than that of JM 10.2 reference software [1] where the quadratic R-Q model is used. The PSNR improvement is up to 0.63 dB and about 0.40 dB on the average for our test sequences. Furthermore, the proposed rate control achieves the target bit rate much more accurately.