Abstract
With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.
Highlights
With higher requirements for video compression, more effective video coding standards have become critical. e Joint Video Experts Team (JVET) has developed a next-generation video coding standard called the H.266/VVC [1]. e H.266/VVC Test Model (VTM) [2] has implemented many novel technologies, which can significantly enhance the coding efficiency
If the complex coding unit (CU) belong to edge CU, the Convolutional Neural Network (CNN) structure based on multifeature fusion is used for classification
We have designed a CNN structure based on multifeature fusion for the case that the edge CU may be misclassified, which utilizes the texture complexity and depth feature of edge CUs as input, thereby improving the accuracy of classification. e CNN classifier is used to classify CUs, which may not be essential for CUs in homogeneous regions. us, before performing adaptive CNN structure and CNN structure based on multifeature fusion, we use heuristic method to avoid unnecessary calculation, which can obtain good efficiency
Summary
With higher requirements for video compression, more effective video coding standards have become critical. e JVET has developed a next-generation video coding standard called the H.266/VVC [1]. e H.266/VVC Test Model (VTM) [2] has implemented many novel technologies, which can significantly enhance the coding efficiency. E H.266/VVC Test Model (VTM) [2] has implemented many novel technologies, which can significantly enhance the coding efficiency. The H.266/VVC uses a quad-tree with nested multitype tree (QTMT) coding architecture for CU partition [3], which shows better coding performance, but leads to the extremely coding computational complexity. The novel technologies, which include QTMT partitioning structure, extended intraprediction mode, PDPC, and MTS, significantly enhance performance of H.266/VVC but lead significantly to the computational complexity. E H.266/VVC proposes some novel technologies for intracoding based on H.265/HEVC and extends some previous methods, where the key concept in these tools is MTT structure [8]. More CU shapes greatly increase the complexity of intraprediction and have longer encoding time but greatly improve the encoding efficiency in H.266/VVC.
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