Abstract
Performance of real-time video processing applications such as surveillance systems, content-based search, is limited by the complexity of video content analysis in the pixel domain. A low complex alternative is to analyse the video in the compressed domain, where content features already available in the compressed video are directly used in the analysis. However, this is achieved at the expense of output precision and reliability, due to compression-efficiency driven feature selection at the encoder. Therefore, video applications could benefit from enhanced reliability of data embedded in the compressed video. In this paper, we present a scalable optimization model that addresses the accuracy of content features in parallel with the conventional rate-distortion optimization criterion. We analyse and optimize rate-distortion performance of video encoder under content description accuracy constrain, using a motion calibrated synthetic data set containing a range of scene and motion complexity levels. Finally, using a natural video data set, we demonstrate that the proposed optimization framework can be used to enhance compressed feature accuracy without incurring a rate-distortion overhead.