Research On Fast Algorithm Techniques For High Efficiency Video Coding

The latest video coding standard High Efficiency Video Coding (HEVC) can provide the same perceptual video quality with50%bitrate reduction compared to H.264/MPEG-4AVC. However, such flexibility of encoding tools introduces great computation burden on HEVC encoder, which is the bottleneck of enabling HEVC into practical use. Hence, research on reducing the computational complexity of HEVC encoder while maintaining the high coding efficiency is crucial for HEVC.It gives a brief introduction of video coding techniques and the development of video coding standards at the beginning of this thesis, and summarizes the HEVC encoding framework. According to the features of HEVC, the key areas for fast algorithms are figured out:fast intra coding unti size selection, fast inter coding unit size decision, and fast motion estimation algorithms.To reduce the computational complexity of HEVC intra encoder, a fast intra coding unit (CU) size selection algorithm based on the statistical learning is proposed. To address this issue, the splitting of CU is modeled as a k-means classification problem. The four dimension vector is formed by using the pixel variance of the four sub-blocks in a CU and selected as the feature for the k-means learning. In such a way, the full search based on the rate distortion cost for all possible prediction units is avoides and the computational complexity is reduced.To fasten the inter CU size decision, a spatio-temporal correlation based fast CU size decision algorithm is proposed. After analyzing the correlation between the current coding tree unit (CTU) and the spatio-temporal neighbor CTU, the best spatio-temporal neighbor CTUs are selected. The depth information of the best spatio-temporal neighbor CTUs are used to predict the depth range of the current CTU. The characterstic called the depth monotonicity between the current CU and the adjacent CU is introduced. Using this characteristic, some specific depths are skipped when performing the CU size selection in the current CU. Thus, the computational complexity of the HEVC encoder can be significantly reduced. The multiple reference frame and the flexible data representation in HEVC increase the complexity of motion estimation. A fast multiple reference frame selection (MRFS) algorithm is proposed by exploiting the correlation among different prediction units (PUs) in the same CU and the correlation between the parent CU and the child CU. After performing the MRFS of2N×2N PU, the rate distortion cost of each reference frame is used to reduce the number of candidate reference frame for other PUs. When the parent CU is SKIP, the best reference frame of current CU is fixed as the best reference frame of the parent CU. Thus, the computational complexity of MRFS is reduced. To reduce the bandwidth of the movig data for motion estimation stage, the author analyzes the influence of the search range for sequences with different resolutions and characteristics. And the intial search ranges for sequences with different resolutions are suggested, and the data bandwidth can be reduced. Finally, the author concludes the new achievements of the whole research and the prospect of the future research.