| 摘要(英) |
In today′s era of rapid advancements in networks and technology, the demand for high-resolution image quality continues to grow. However, the massive data generated by high-resolution images requires more efficient compression technologies to handle. H.266/VVC introduces numerous advanced techniques, such as multi-type division of square and rectangular Coding Units (CUs) and Rate-Distortion Optimization (RDO). While these innovations improve compression efficiency, they significantly increase the computational complexity of encoding.
This paper combines traditional feature-based methods with machine learning and deep learning techniques, utilizing Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest Classifiers in the CU division of VVC. Our two-stage VVC first employs SVM and CNN to divide square CUs in the first stage and then uses Random Forest Classifiers to further process rectangular CUs in the second stage. However, studies have revealed that CNN has issues with missing certain MT division patterns when predicting division modes, leading to a decline in encoding performance.
To address this issue, we propose a Sobel Operator-based criterion to detect texture directions in images and assist in division decisions. Experimental results show that incorporating the Sobel Operator in the first stage leads to a BDBR increase of only 0.42%, while saving 26.48% of encoding time. The two-stage VVC achieves an average BDBR increase of just 0.95% while saving 61.1% of encoding time. Compared to the original two-stage VVC, our improvements significantly enhance encoding performance with only a slight increase in computational time.
Furthermore, we optimize the algorithm by using the decision values of the SVM as the basis for adjustable thresholds. Through the design of adjustable thresholds, we effectively reduce the number of CUs entering the CNN, thereby terminating the CU division process earlier. The adjustable thresholds allow users to flexibly balance image quality and encoding time according to different application needs, achieving highly efficient compression performance. |
| 參考文獻 |
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