SIFT feature-preserving bit allocation for H.264/AVC video compression
Compression artifacts in low-quality videos strongly influence the performance of feature matching algorithms. In order to achieve reasonable feature matching performance even for low bit rate video, we propose to allocate the bit budget during compression such that the important features are preserved. Specifically, we present two bit allocation approaches to preserve the strongest SIFT features for H.264 encoded videos. For both approaches, we first categorize the Macroblocks in a Group of Pictures into several groups according to the scale specific characteristics of SIFT features. In our first approach a novel R-D model based on the matching score is applied to allocate the bit budget to these groups. In our second approach, in order to reduce the computational complexity, we analyze the detector characteristics of correctly matched pairs and propose a R-D optimization method based on the repeatability metric. Our experiments show that both approaches achieve better feature preservation when compared to standard video encoding which is optimized for maximum picture quality. The proposed approaches are fully standard compatible and the encoded videos can be decoded by any H.264 decoder.