The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality.

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In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the Versatile Video Coding (VVC) encoder for VANETs. In this paper, a low complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra prediction mode optimization technique based on CU texture complexity aims to combine intra prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially cut down on encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be cut by up to 53.29\% when compared to the VVC reference model, although the average BD-rate is only raised by 1.26\%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders seen in connected vehicle applications.