To date, Versatile Video Coding (VVC) has a more magnificent overall performance than High Efficiency Video Coding (HEVC). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can substantially enhance video coding quality in VVC. However, the coding gain also leads to a greater coding complexity. Therefore, this letter proposes a Fast Decision Scheme Based on Structural Similarity Index Metric Variation (FDS-SSIMV) to solve this problem. Firstly, the Structural Similarity Index Metric Variation (SSIMV) characteristic among the sub coding units of the spit mode is illustrated. Next, to evaluate the SSIMV value, SSIMV measure strategies are designed for different split modes in this letter. Then, the desired split modes are selected by the SSIMV values. Experimental results show that the proposed method achieves 64.74\% average encoding Time Saving (TS) with a 2.79\% Bj$\varnothing$ntegaard Delta Bit Rate (BDBR), outperforming the benchmarks.

Arm DevSummit was one of several virtual tech conferences and launch events taking place over the past month week and it included everything from a fireside chat with the CEOs of Arm and Nvidia about the proposed acquisition to new technology announcements and the deep dive training tracks one would expect. One of the most interesting announcements was around a new platform of cores for SoCs specifically targeting autonomous machines. While there is a growing focus on autonomous vehicles, few companies look at the broader opportunity for autonomous control across a wide variety of platforms that include autonomous systems for materials handling and production for everything from consumer products to agriculture. Arm announced a complete platform that can span a wide range of applications that meet the safety critical requirements of autonomous systems. As Arm expanded its technology offerings in mobile, it began releasing a complete platform of compute cores and related technologies together as a single platform.