# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 6 Conclusions  
In this paper, we propose a fast content-adaptive CU size selection mechanism using weighted SVMs. The proposed SVM models are utilised at two different levels in the HEVC encoding chain. First, level-1 SVM models are used before the encoding of the current CU to determine whether to skip the current level or to encode the current CU level. This early CU size decision eliminates the evaluation of unnecessary CU sizes, thus, enabling the proposed algorithm to achieve significant encoding time reductions. The CU size decisions that cannot be classified as \"split\" or \"non-split\" with the level-1 SVM models are passed on to level-2 SVM model which operates once the current CU level is evaluated for all the possible PU modes. The level 2 SVM model then decides whether to continue the recursive splitting of the CU or move on to the next coding block.  
The proposed method collects data and builds models in run-time during the encoding process, which makes the CU split decisions derived using the proposed algorithm dynamic and content-adaptive--a crucial advantage compared to the state-of-the-art methods that rely on large amounts of past data. The complexity control parameter introduced in this work allows the proposed algorithm to control the amount of decisions taken by the SVM models and traditional RD optimisation, thereby effectively trading-off the computational complexity to the coding efficiency. The experimental results reveal an average encoding time saving of up to 52.38%, with only 1.19% average BDBR increase (i.e., increase in bitrate to a similar video quality by the proposed algorithm) compared to  
Figure 6: RD performance of three sample video sequences. Top row corresponds to RD graphs drawn with PSNR as the quality metric. Bottom row corresponds to the VQM as the quality metric. RD: Rate-Distortion.  
the HM16.1 encoded bit streams, when using PSNR as the quality measurement for the reconstructed videos. Similarly, the proposed algorithm demonstrates 1.52% average BDBR increase compared to HM encoded streams when measuring the users' perceived video quality using VQM. Therefore, the proposed algorithm provides an effective alternative compared to the state-of-the-art algorithms to reduce the encoding time complexity of HEVC intra-coding in emerging multimedia applications.  
The future works will focus on extending the framework for HEVC inter-prediction to facilitate fast content-adaptive HEVC video coding.  
Conceptualization, B.E. and T.M.; methodology, B.E; software, B.E.; validation, B.E. and T.M.; formal analysis, B.E. and T.M.; investigation, B.E.; resources, A.F. and C.H.; data curation, B.E.; writing--original draft preparation, T.M.; writing--review and editing, T.M., B.E., and C.H.; visualization, T.M.; supervision, T.M., A.F.; project administration, A.F.; funding acquisition, A.F. and C.H.  
This research was funded by the CONTENT4 ALL project, which is funded under European Commission's H2020 Framework Program (Grant number: 762021) and European Social Fund (ESF) grant under KESSII WEST - Collaborative project between Cardiff Metropolitan University and Ultranyx Ltd.  
The authors declare no conflict of interest.