Analytical Distortion Aware Video Coding for Computer Based Video Analysis

With the development of artificial intelligence, more and more multimedia applications for various tasks have emerged in our daily life. Meanwhile, as one of the main information sources of the applications, a huge amount of video data has been being generated by portable or mounted cameras in daily basis for varying purposes including surveillance, in which case we may need computers to "watch" videos to save labor cost. However, most video coding standards are designed for the highest human perceptual quality given a bit rate by minimizing a fidelity cost function (e.g., mean squared error, MSE), assuming the content will be consumed by human beings. In view of the above considerations, this paper proposes a new rate-analytical-distortion optimization method (RADO) for video analysis. Specifically, we consider moving object detection as the analysis task. Accordingly, we develop a novel rate analytical distortion (RAD) model for video coding, where the analytical distortion is related to the object detection performance expressed in terms of F-measure. As shown in the experimental results, the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion) with a slight bit rate increase.