Content-adaptive motion estimation for efficient video compression

Abstract

Motion estimation is the most important step in the video compression. Most of the current video compression systems use forward motion estimation, where motion information is derived at the encoder and sent to the decoder over the channel. Backward motion estimation does not derive an explicit representation of motion at the encoder. Instead, the encoder implicitly embeds the motion information in an alternative subspace. Most recently, an algorithm that adopts least-square prediction (LSP) for backward motion estimation has shown great potential to further improve coding efficiency. Forward motion estimation and backward motion estimation have both their advantages and disadvantages. Each is suitable for handling some specific category of patterns. In this paper, we propose a novel approach that combines both forward motion estimation and backward motion estimation in one framework to adaptively exploit the local motion characteristics in an arbitrary video sequence, thus achieving better coding efficiency. We refer to this as Content-Adaptive Motion Estimation (CoME). The encoder in the proposed system is able to adjust the motion estimation method in a rate-distortion optimized manner. According to the experimental results, CoME reduces the data rate in both lossless and lossy compression.