Laser & Optoelectronics Progress, Volume. 55, Issue 1, 11006(2018)
Video Fingerprint Algorithm Based on Spatio-Temporal Deep Neural Network
Figures & Tables(10)
Table 1. Distortion types and parameters setting
View tableTable 1. Distortion types and parameters setting
Distortion Description Compression Encoder: XVid, frame rate: 25 frame/s, bit rate: 256 kb/s, fixed resolution: 480×320 Median filtering Filter size ∈[10,20] Gaussian noise Zero mean, variance ∈[0.1,0.5,1] Rotation+croping Pixel ∈[2,5,10] Histogram equalization Number of gray levels ∈[16,32,64] Frame dropping Delete 25% frames then linearly interpolate Frame resizing Ratio ∈[0.2,4] Joint spatio-temporal distortion 1 Combine median filtering (filter size is 10) and frame dropping (25%) Joint spatio-temporal distortion 2 Combine rotation (pixel is 5) and frame dropping (25%)
Table 2. Comparison of the F1 scores
View tableTable 2. Comparison of the F1 scores
Distortion Proposed algorithm CRBM algorithm CGO algorithm SGM algorithm 3D-DCT algorithm Rash algorithm Compression 0.966 0.907 0.801 0.929 0.889 0.845 Filtering 0.998 0.994 0.887 0.978 0.991 0.966 Noise 0.999 0.999 0.813 0.890 0.896 0.624 Rotation 0.999 0.988 0.534 0.939 0.838 0.950 Equalization 0.996 0.980 0.839 0.836 0.851 0.519 Dropping 0.999 0.999 0.996 0.994 0.999 0.999 Resizing 0.999 0.999 0.903 0.994 0.998 0.948 Joint distortion 1 0.998 0.996 0.941 0.985 0.997 0.981 Joint distortion 2 0.999 0.993 0.516 0.968 0.888 0.965 Overall 0.995 0.982 0.783 0.915 0.894 0.793
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Wang Dongdong, Li Yuenan. Video Fingerprint Algorithm Based on Spatio-Temporal Deep Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11006
Paper Information
Category: Image Processing
Received: Jul. 18, 2017
Accepted: --
Published Online: Sep. 10, 2018
The Author Email: Wang Dongdong (wddtju@aliyun.com)
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