Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling
Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling
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DOI：
10.1109/tip.2014.2352036
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发表时间：
2014-08
影响因子：
10.6
通讯作者：
Xianguo Zhang;Yonghong Tian;Tiejun Huang;Siwei Dong;Wen Gao
中科院分区：
文献类型：
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作者：
Xianguo Zhang;Yonghong Tian;Tiejun Huang;Siwei Dong;Wen Gao
对于实时和低延迟的视频监控和电话会议应用，新的视频编码标准HEVC可以获得比H.264/AVC更高的编码效率。然而，我们仍然认为，HEVC低延迟编码器中的分层预测结构仍然没有充分利用监控和会议视频通常由固定摄像机捕获的特点。在这种情况下，可以使用从原始输入帧建模的背景图片(G图片)来进一步提高HEVC低延迟编码的效率，同时降低复杂度。因此，针对这些背景建模的视频，我们提出了一种HEVC分层预测和编码的优化方法。首先，对如何利用G图优化分层预测结构和分层量化进行了实验和理论分析。在此基础上，提出将G图像作为长期参考帧进行编码以改善背景预测，并提出了一种基于G图像的比特分配算法来提高编码效率。同时，根据背景像素和前景像素在编码单元(CU)中的比例，提出了一种自适应加速算法，将每个CU分成不同的类别，然后采用不同的加速策略来降低编码复杂度。为了评估其性能，在HEVC测试模型上进行了大量的实验。实验结果表明，该方法在监控视频上平均节省39.09%的比特数，编码复杂度降低43.63%，而在会议视频上平均节省5.27%和43.68%的编码复杂度。
For the real-time and low-delay video surveillance and teleconferencing applications, the newly video coding standard HEVC can achieve much higher coding efficiency over H.264/AVC. However, we still argue that the hierarchical prediction structure in the HEVC low-delay encoder still does not fully utilize the special characteristics of surveillance and conference videos that are usually captured by stationary cameras. In this case, the background picture (G-picture), which is modeled from the original input frames, can be used to further improve the HEVC low-delay coding efficiency meanwhile reducing the complexity. Therefore, we propose an optimization method for the hierarchical prediction and coding in HEVC for these videos with background modeling. First, several experimental and theoretical analyses are conducted on how to utilize the G-picture to optimize the hierarchical prediction structure and hierarchical quantization. Following these results, we propose to encode the G-picture as the long-term reference frame to improve the background prediction, and then present a G-picture-based bit-allocation algorithm to increase the coding efficiency. Meanwhile, according to the proportions of background and foreground pixels in coding units (CUs), an adaptive speed-up algorithm is developed to classify each CU into different categories and then adopt different speed-up strategies to reduce the encoding complexity. To evaluate the performance, extensive experiments are performed on the HEVC test model. Results show our method can averagely save 39.09% bits and reduce the encoding complexity by 43.63% on surveillance videos, whereas those are 5.27% and 43.68% on conference videos.