Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  deals with data hiding in compressed video. Unlike data hiding in images and raw video which operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis,  target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their associated macroblock prediction error, which is different from the approaches based on the motion vector attributes such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve robustness while maintaining a low prediction error level. The secret message bitstream is embedded in the least significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based on the aforementioned criteria, the proposed method is found to perform ll and is compared to a motion vector attribute-based method from the literature.
['Hussein A. Aly']
I am writing a research paper on a idea Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error
[{"from": "human", "value": "I am writing a research paper on a idea Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  deals with data hiding in compressed video. Unlike data hiding in images and raw video which operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis,  target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their associated macroblock prediction error, which is different from the approaches based on the motion vector attributes such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve robustness while maintaining a low prediction error level. The secret message bitstream is embedded in the least significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based on the aforementioned criteria, the proposed method is found to perform ll and is compared to a motion vector attribute-based method from the literature."}]