HEVC压缩域的视频摘要关键帧提取方法

为了提高关键帧提取的准确率,改善视频摘要的质量,提出了一种HEVC压缩域的视频摘要关键帧提取方法。首先,对视频序列进行编解码,在解码中统计HEVC帧内编码PU块的亮度预测模式数目。然后,特征提取是利用统计得到的模式数目构建成模式特征向量,并将其作为视频帧的纹理特征用于关键帧的提取。最后,利用融合迭代自组织数据分析算法(ISODATA)的自适应聚类算法对模式特征向量进行聚类,在聚类结果中选取每个类内中间向量对应的帧作为候选关键帧,并通过相似度对候选关键帧进行再次筛选,剔除冗余帧,得到最终的关键帧。实验结果表明,在Open Video Project数据集上进行的大量实验验证,该方法提取关键帧的精度为79.9%、召回率达到93.6%、F-score为86.2%,有效地改善了视频摘要的质量。

Video Summarization Key Frame Extraction Method for HEVC Compressed Domain

In order to improve the accuracy of key frame extraction, and improve the quality of video summaries, a video summarization key frame extraction method for HEVC compressed domain is proposed. Firstly, the video sequence is coded and decoded, and the number of luminance prediction modes of the HEVC intra-coded PU block is counted in the decoding. Secondly, the feature extraction is constructed by using the number of patterns obtained by statistics as a pattern feature vector and used as a texture feature of the video frame for key frame extraction. Finally, the pattern feature vector is clustered by adaptive clustering algorithm, which the fusion iterative self-organizing data analysis algorithm (ISODATA). The frames corresponding to the intermediate vector in each class is selected as the candidate key frames in the clustering result, and the candidate key frames are again filtered by the similarity, which the redundant frames are eliminated to obtain the final key frames. The experimental results show that a large number of experiments on the Open Video Project dataset indicate that the precision of the key frames extraction is 79.9%, the recall rate is 93.6%, and the F-score is 86.2%, which effectively improves the quality of the video summarization.