With the development of deep learning, neural video compression (NVC) is gaining increasing interest and is currently under standardization in the AVS (Audio Video Coding Standard). Skip mode coding, a crucial component in the traditional video compression, is also adopted in AVS NVC. Although the skip mode takes a large portion in encoding the motion and residual features in the NVC, there is a lack of investigation on the rate distortion performance of the skip mode coding. In this paper, we first thoroughly investigate the rate and distortion distribution in the skip mode coding within a spatial frame and under different temporal reference qualities, and demonstrate that distortion distribution changes spatially and temporally. Therefore, a rate-distortion optimized skip mode coding (RDOSkip) framework is proposed, where the rate and distortion in the skip mode coding is formulated based on a statistical distribution predicted by the hyperprior and the Bayes' theorem. An adaptive skip mode decision method based on spatial-temporal joint RD optimization is developed for the skip mode coding among channels and frames in a group of pictures. It addresses the limitation of the rigid fixed-distortion distribution assumption in skip mode coding within the existing methods that rely on a fixed skip threshold. The temporal skip mode decision method has been adopted in the ongoing AVS NVC standard and incorporated into the reference software. The proposed RDOSkip can be directly used in the inference without any training and with negligible complexity increase. Experimental results further demonstrate that the proposed RDOSkip can improve the performance by 0.90% and 1.29% on AVS and HEVC datasets, respectively, in terms of BD-rate saving.

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