Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Due to the complex environment conditions, many surveillance videos are captured from cameras which are influenced by shaking more or less. This presents a significant challenge for background-modeling-based video coding since it is difficult to generate good background frames from such shaking videos. To solve this problem, this paper proposes a global motion compensation method using motion vectors (MV-GMC) for shaking surveillance video coding. In the proposed MV-GMC method, more accurate motion vectors (MVs) are extracted from HEVC encoder to estimate the global motion model in an efficient way, and  compensate each frame before background modeling. Then the compensated frames are used to model a good background frame for surveillance video coding. Compared with the optical-flow-based GMC (OPT-GMC) method which can be used to obtain more precise motion compensation, the proposed MV-GMC method has a comparable coding performance but a much lor computational complexity. Experiments on our surveillance video sequences show that the proposed MV-GMC method has significantly improved the coding performance by decreasing BD rate 49.83% over HM 12.0 on average while OPT-GMC can save 49.84% BD rate. The MVGMC method also saves 92.71% background modeling time compared with the OPT-GMC method.
['Lin Ding', 'Yonghong Tian', 'Hongfei Fan', 'Yaowei Wang', 'Tiejun Huang']
I am writing a research paper on a idea High-Efficiency Coding for Shaking Surveillance Videos Based on Global Motion Compensation
[{"from": "human", "value": "I am writing a research paper on a idea High-Efficiency Coding for Shaking Surveillance Videos Based on Global Motion Compensation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Due to the complex environment conditions, many surveillance videos are captured from cameras which are influenced by shaking more or less. This presents a significant challenge for background-modeling-based video coding since it is difficult to generate good background frames from such shaking videos. To solve this problem, this paper proposes a global motion compensation method using motion vectors (MV-GMC) for shaking surveillance video coding. In the proposed MV-GMC method, more accurate motion vectors (MVs) are extracted from HEVC encoder to estimate the global motion model in an efficient way, and  compensate each frame before background modeling. Then the compensated frames are used to model a good background frame for surveillance video coding. Compared with the optical-flow-based GMC (OPT-GMC) method which can be used to obtain more precise motion compensation, the proposed MV-GMC method has a comparable coding performance but a much lor computational complexity. Experiments on our surveillance video sequences show that the proposed MV-GMC method has significantly improved the coding performance by decreasing BD rate 49.83% over HM 12.0 on average while OPT-GMC can save 49.84% BD rate. The MVGMC method also saves 92.71% background modeling time compared with the OPT-GMC method."}]