Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

Abstract

In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality.

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

“…Therefore, the scheme to obtain coding complexity reduction at the expense of bit rate is not desirable. Compared with the algorithm in [35], the algorithm in this paper not only reduces coding complexity by 0.82% but also decreases BDBR by 0.13%, further improving coding performance. A comparison of the coding performance data is presented in Figure 11.…”

“…To further evaluate the performance of the proposed algorithm, this study objectively analyzes and compares it with representative intra-frame coding algorithms: references [27,34,35]. The comparison of algorithm performance is presented in Table 6.…”

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

“…The QP settings are {22, 27, 32, 37}. To assess the performance of the fast CU division decision method, we employed the evaluation criteria outlined in three scenarios as discussed in [33][34][35]. The algorithm's performance was evaluated based on two metrics: Bjøntegaard delta bit rate (BDBR) and time saved (∆T).…”

“…To further assess the algorithm's performance, the algorithm was analyzed in comparison with other state-of-the-art algorithms (i.e., methods proposed in studies [33][34][35]). BDBR and ∆T were the evaluation metrics used to measure the performance of rate distortion (RD).…”

“…BDBR and ∆T were the evaluation metrics used to measure the performance of rate distortion (RD). The three schemes of [33][34][35] were chosen because they are closely related to our experimental setup; the experiments were all implemented on the VTM-10.0 platform and therefore these schemes are considered to be highly representative of the current research area. Table 5 presents a comprehensive comparison of BDBR and ∆T among the three schemes discussed in [33][34][35].…”