1. HEVC intra frame based compressed domain video summarization
- Author
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Fu Linjun, Zhengnan Liu, Fengsui Wang, Furong Liu, Shuming Zhu, and Qisheng Wang
- Subjects
Normalization (statistics) ,0209 industrial biotechnology ,Computer science ,business.industry ,Feature vector ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Word error rate ,Pattern recognition ,02 engineering and technology ,Intra-frame ,Automatic summarization ,020901 industrial engineering & automation ,Image texture ,Histogram ,0202 electrical engineering, electronic engineering, information engineering ,Chrominance ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Decoding methods - Abstract
In order to overcome the shortcomings of the poor quality of the final generated video summary by reason of the incomplete selection of video feature points, a compressed domain video summarization generation algorithm is proposed based on HEVC intra-frame coding. Firstly, the weighted luminance and chrominance mode numbers are counted at the decoding end, respectively. Then, the feature vectors composed of the above two mode numbers are fused and normalized to obtain a mode feature histogram. Secondly, the normalized mode feature histograms are assigned different weight factors, and the two are merged into a new mode feature histogram model to reflect the texture features of the image. Finally, the histogram difference method is used to determine the similarity between two-frame mode feature histograms, and ultimately the histogram classification is used to generate the video summaries. The experimental results show that the accuracy of keyframe extraction on the Open Video Project dataset is 0.82, the error rate is 0.64. And that, the average recall rate achieves 82.0%, and the average F-score is 73.3%. The key frames extracted by the algorithm better reflect the image texture, and the video summary quality is further improved.
- Published
- 2019
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