Four Key Research Papers for RAE2020



  1. Hongbin Zhang, Chang-Hong Fu, Yui-Lam Chan, Sik-Ho Tsang, and Wan-Chi Siu, “Probability-based Depth Mode Skipping Strategy and Novel VSO Metric for DMM Decision in 3D-HEVC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, issue 2, pp. 513-527, Feb. 2018, USA.

Description: This work presents two efficient techniques for depth intra mode decision by investigating the statistical characteristics of variance distributions in the two partitions of depth modelling mode (DMM), a simple but efficient criterion based on the squared Euclidean distance of variances (SEDV) is suggested to evaluate RD costs of the DMM candidates instead of the time-consuming VSO process. Then, a probability-based early depth intra mode decision (PBED) is proposed to select only the most promising mode and make the early determination of using SDC based on the low complexity RD-Cost in rough mode decision. Experimental results show that the proposed algorithm with these two new techniques provides 33%-48% time reduction with little drops of the coding performance compared with the state-of-the-art algorithms. This is the main paper published from work carried out on General Research Fund (GRF 2013/2014): GRF PolyU 5119/13E.

  1. Tsz Kwan Lee, Yui-Lam Chan, and Wan-Chi Siu, “Adaptive Search Range for HEVC Motion Estimation based on Depth Information,” IEEE Transactions on Circuits and Systems for Video Technology vol. 27, issue 10, pp. 2216-2230, Oct. 2017, USA.

Description: This is the first paper using depth information for HEVC motion estimation. This work exploits the unique characteristics in depth map when it is adopted in texture coding. Results reveal that, compared to the full search approach, the proposed algorithm can reduce the complexity by 93% on average whereas the coding efficiency can be maintained. It will have a remarkable impact on the advancement of the fast algorithm on video coding technology. This work has also formed the fundamental basis for developing how to use depth map in HEVC video coding.

  1. Sik-Ho Tsang, Yui-Lam Chan, Wei Kuang, and Wan-Chi Siu, “Reduced-Complexity Intra Block Copy (IntraBC) Mode for HEVC Screen Content Coding,” IEEE Transactions on Multimedia, vol. 21, no. 2, pp.269-283, Feb. 2019, USA.

Description: This work presents a number of fast Screen Content Coding (SCC) techniques. The experimental results show that the encoding time can be reduced by 17.07% on average, while there is only 1.52% increase in BDBR compared with the conventional HEVC SCC extension. Our proposed algorithm is mainly designed for coding the screen contents. Results also demonstrated that it can work in conjunction with other recent frameworks for a remarkable complexity reduction. This work has also formed the fundamental basis for developing other new video formats such as light-field videos. This has laid down the foundation for our successful GRF bid in the following year (GRF 2017/2018: GRF PolyU 152112/17E).

  1. Wei Kuang, Yui-Lam Chan, Sik-Ho Tsang, and Wan-Chi Siu, “Machine Learning Based Fast Intra Mode Decision for HEVC Screen Content Coding Via Decision Trees” paper accepted, to appear in IEEE Transactions on Circuits and Systems for Video Technology.

Description: Screen content video is one of the hottest topics in video coding community. This paper is the first few papers using the learning approach for coding the new screen content video. The BDBR and complexity performance are now the best in the literature. It will have a remarkable impact on the advancement of the screen content coding technology. This work has also formed the fundamental basis for developing other new video formats such as light-field and 360 degree videos. We can further extent this concept to these application domains. Up to now, no work for these areas using the learning approach for coding these types of video. We believe it is a fruitful direction for video coding community. As a result, this has led to a new GRF project (GRF 2018/2019: GRF PolyU 152069/18E).