Damaged video reconstruction using inpainting

2019 ◽  
pp. 201-207
Author(s):  
Kamesh Sonti ◽  
K. Rasool Reddy
Keyword(s):  
Video Reconstruction

The infrared moving target extraction and fast video reconstruction algorithm

2019 ◽  
Vol 97 ◽  
pp. 85-92 ◽  
Author(s):  
Shi Qiu ◽  
Ying Tang ◽  
Yun Du ◽  
Song Yang
Keyword(s):  
Reconstruction Algorithm ◽  
Moving Target ◽  
Target Extraction ◽  
Video Reconstruction

scholarly journals Video steganography based on DCT psychovisual and object motion

2020 ◽  
Vol 9 (3) ◽  
pp. 1015-1023 ◽  
Author(s):  
Muhammad Fuad ◽  
Ferda Ernawan
Keyword(s):  
Video Quality ◽  
Object Motion ◽  
Multimedia Data ◽  
Secret Message ◽  
Hiding Capacity ◽  
Video Steganography ◽  
Limited Bandwidth ◽  
Video Frames ◽  
Dct Coefficients ◽  
Video Reconstruction

Steganography is a technique of concealing the message in multimedia data. Multimedia data, such as videos are often compressed to reduce the storage for limited bandwidth. The video provides additional hidden-space in the object motion of image sequences. This research proposes a video steganography scheme based on object motion and DCT-psychovisual for concealing the message. The proposed hiding technique embeds a secret message along the object motion of the video frames. Motion analysis is used to determine the embedding regions. The proposed scheme selects six DCT coefficients in the middle frequency using DCT-psychovisual effects of hiding messages. A message is embedded by modifying middle DCT coefficients using the proposed algorithm. The middle frequencies have a large hiding capacity and it relatively does not give significant effect to the video reconstruction. The performance of the proposed video steganography is evaluated in terms of video quality and robustness against MPEG compression. The experimental results produce minimum distortion of the video quality. Our scheme produces a robust of hiding messages against MPEG-4 compression with average NC value of 0.94. The proposed video steganography achieves less perceptual distortion to human eyes and it's resistant against reducing video storage.

Significant Features Selection Resistant to Temporal Distortions

2013 ◽  
Vol 284-287 ◽  
pp. 3154-3158
Author(s):  
Ta Te Lu
Keyword(s):  
Mean Value ◽  
Video Sequences ◽  
Detection Accuracy ◽  
Features Selection ◽  
Maximum Eigenvalue ◽  
Block Partition ◽  
Local Variance ◽  
Video Frames ◽  
The Mean ◽  
Video Reconstruction

Most patterns in continuous video sequences are similar. Temporal distortion, e.g. frames dropping, insertions, transposition, is a challenging issue for video reconstruction to find the actual missing positions in video sequences. The aim of this paper is to raise the detection accuracy and synchronize video frames back to original positions following temporal synchronization distortions. The successive video frames have similar statistics but the statistics in some local regions may differ from one another. Therefore, the block partition is partitioned into non-overlapping blocks by each frame, and then the local variance is calculated and taken as the block feature in each block. For most of the video frames, the pixels within the frame blocks are correlated and the maximum eigenvalue will be far from other eigenvalues. In this case, the maximum eigenvalue is set as the dominated block feature. For less correlated blocks, the values of the eigenvalues will be a little closer. In this case, the mean value of the eigenvalues represents the dominated block feature. Then, the sum of variance is regarded as the frame feature to calculate from these selective dominated blocks. Simulation results show the proposed methods are robust in evaluating the missing positions against temporal distortions.

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