Research

  • High Efficiency Video Coding with Multi-Order-Residual Prediction
    2010
     A novel multi-order-residual-prediction (MORP) coding approach is proposed to improve spatial prediction efficiency in video coding. We observe that the compression ratio of a video coding algorithm depends on the nature of sequences as indicated by the ratio between inter and intra blocks in the bit-stream. When the percentage of intra blocks increases, the prediction efficiency decreases, thus leading to a poorer coding gain. In other words, one bottleneck of video coding lies in poor intra prediction efficiency. To address this issue, we propose an MORP coding scheme that adopts a second-order prediction scheme after the traditional first-order prediction. Different prediction techniques are adopted in different stages to tailor to the nature of the corresponding residual signals. The proposed MORP scheme outperforms H.264/AVC by a significant margin for the intra block coding and, thus, improves the overall coding efficiency.
  • Parallel Motion Estimation on GPU
    2010
     Motion estimation (ME) is consider as the most complicated module in the video coding. In this research, we parallelize the ME process by macroblocks based on the graphic processing unit (GPU) architecture. The performance of ME is also optimized base on two factors: the occupancy of GPU, and the number of memory transactions between global and local memory. (full text)
  • Adaptive Motion Vector Position and Search Range Prediction for Low Complexity Video Coding
    2009-2010
    An adaptive motion vector (MV) position and search range (SR) prediction algorithm in motion estimation for video encoding is proposed in this work with an objective to achieve excellent coding performance with reduced memory access bandwidth. In modern fast motion search algorithms, a motion vector predictor (MVP) is obtained from the motion vectors of spatially and temporally neighboring blocks, which is called the predictive MV set. By exploiting the relationship between the variance of the predictive MV set and the SR, we develop an adaptive SR selection algorithm. That is, a larger variance implies lower accuracy of the MVP and, thus, a larger SR. (full text)