Since the emergence of high-quality multimedia processing applications such as video streaming, digital editing, archiving, etc. these days, an intra coding rate control (RC) is becoming an indispensable and important technology. In this paper, a frame-level intra RC scheme for Versatile Video Coding (VVC) using a Lagrange multiplier adjustment (LMA) is proposed. The VVC test model (VTM) uses an R-λ model-based rate control. However, the estimation performance of target bits based on an R-λ-QP relation is decreased because the distortion dependencies among consecutive frames are not considered especially for intra RC. Thus, in a rate-distortion optimization (RDO) based encoding, the λ values determined for given quantization parameter (QP) values should be elaborately controlled to increase the target bits estimation performance. In our work, we focus on the intra RC scheme by taking advantage of particle-filtering-based prediction (PFP) for distortion estimates, and precise per-frame λ values can be derived for an appropriate RDO process that can lead to small bit-fluctuations. Our extensive experimental results demonstrate that our RC scheme using the per-frame LMA is superior to the default RC (VTM-16.0rc1) method and the state-of-the-art RC methods withsignificantmargins of average 15.57%, 15.31% and 31.13% improvements in terms of the normalized root mean square error (NRMSE) for All Intra (AI) configuration of VVC, respectively.

In this paper, we propose a new R/D estimation method for intra-predictive coding with deep block partitioning structures. In our proposed R/D prediction, we adopt a particle filtering based prediction (PFP) to precisely predict intermediate R/D estimates for the next frame in a stochastic manner, which helps increasing the prediction accuracy of fast changing R/D values. Then, based on the intermediate R/D estimates by PFP, we infer an optimal model parameter of the TC's probability density function (pdf) via convex optimization. We found that the proposed method brings about more stable R/D estimation performance thanks to both the improved prediction accuracy using the PFP for abrupt changes in true R/D values and the precise estimation of the optimal model parameter. Experimental results show that our method significantly reduces the normalized root mean square error from average 3.17 to 0.79 (74.90% reduction) for rate and from average 2.32 to 0.82 (64.61% reduction) for distortion, compared to the state-of-the art method.