LCU-level Rate-Distortion Optimization for Versatile Video Coding

Rate-distortion curve is greatly influenced by the texture and motion compensation. Therefore, using a uniform Lagrangian multiplier (lambda, λ to all the LCUs in a picture may not achieve the optimal coding performance. To address the challenging problem on how to formulate the appropriate λ to an individual LCU, this paper proposes a LCU-level Lagrangian multiplier adaption (LLMA) algorithm. Firstly, a motion compensation information entropy (MCIE) of 16 × 16 block is calculated by obtaining a simple binary information entropy from the source. Secondly, MCIE is normalized to a standard normal distribution, on the basis of the strong assumption that the video sources obey the Gaussian distribution. Thirdly, an adaption factor is assigned to a LCU, which multiples on the picture-level λ from the system calculation. Experiments show that the proposed LLMA algorithm achieves remarkable performance in low-delay and random-access configurations, with the resulting achievements of 1.81% BD-Rate gains, compared with VTM 13.0 at low-delay common test condition.