A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC
: In the latest generation of video coding standard – H.265/HEVC, the partition of Coding Tree Unit (CTU) into CU (Coding Unit) is a very critical yet time-consuming component. Traditional methods find the optimum partition mode for each CTU through iterative and exhaustive search, which is a very time-consuming process and hinders its application to real-time video streaming scenarios. In this research, we explored and implemented a machine learning based method to avoid the exhaustive search and to improve the performance of encode/decode by optimizing the partition of prediction block in the coding unit. Our results in coding unit split pattern prediction show a significant performance improvement in terms of processing time.
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A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC
Semantic Scholar · Computer Science · 2020
Abstract
: In the latest generation of video coding standard – H.265/HEVC, the partition of Coding Tree Unit (CTU) into CU (Coding Unit) is a very critical yet time-consuming component. Traditional methods find the optimum partition mode for each CTU through iterative and exhaustive search, which is a very time-consuming process and hinders its application to real-time video streaming scenarios. In this research, we explored and implemented a machine learning based method to avoid the exhaustive search and to improve the performance of encode/decode by optimizing the partition of prediction block in the coding unit. Our results in coding unit split pattern prediction show a significant performance improvement in terms of processing time.