Contentadaptive motion estimation for efficient video compression 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 leastsquare 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 ContentAdaptive Motion Estimation (CoME). The encoder in the proposed system is able to adjust the motion estimation method in a ratedistortion optimized manner. According to the experimental results, CoME reduces the data rate in both lossless and lossy compression.

Embedded to lossless coding of motion compensated prediction residuals in lossless video coding Lossless video coding is useful in applications where no loss of information or visual quality is tolerable. In embedded to lossless coding an encoded video stream can be decoded into any bit rate up to the lossless bit rate, which is quite useful in numerous applications. In this paper, the research we present is based on lossless video coding, which uses motion compensated prediction to eliminate temporal redundancy. We specifically address the problem of embedded to lossless coding of the motion compensated prediction residuals. Since the statistical properties of the residuals are different from still images, the best wavelet bases for still images do not perform well for residuals. Since residuals contain a higher portion of high frequency information in high motion regions, a fixed transform for the entire frame is not very efficient. We introduce a spatially adaptive wavelet transform method, which takes the local frame statistics into account before choosing the wavelet base. This transform technique provided the best performance for most of the test frames.