Video compression, crucial for efficient multimedia storage and transmission, benefits from the Versatile Video
Codec (VVC), excelling in compression efficiency. However, rising resolutions and complexities strain computational
resources. This paper delves into dynamic mode parameter control within VVC to optimize compression and complexity
balance. By adapting VVC's mode parameters based on content characteristics, network conditions, and quality requirements
using machine learning and scripting algorithms, we optimize encoding parameters in real-time. Through extensive
experimentation, we demonstrate that this approach significantly enhances VVC's flexibility for high-resolution streaming and
real-time communication, while also shedding light on the potential of integrating machine learning into video coding pipelines
for adaptive compression. Overall, this research not only advances understanding of VVC but also offers a promising solution
for addressing the evolving demands of multimedia applications where balancing compression efficiency and computational
complexity is crucial.
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[7] Video sample from https://www.fastvdo.com/H.264.html
[8] VVC Encoder referencehttps://www.hhi.fraunhofer.de/fileadmin/Departments/VCA/MC/VVC/vvenc-v0.2.1-v1.pdf
