Bohan Li
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This paper proposes a novel bi-directional motion compensation framework that extracts existing motion information associated with the reference frames and interpolates an additional reference frame candidate that is co-located with the current frame. The approach generates a dense motion field by performing optical flow estimation, so as to capture complex motion between the reference frames without recourse to additional side information. The estimated optical flow is then complemented by transmission of offset motion vectors to correct for possible deviation from the linearity assumption in the interpolation. Various optimization schemes specifically tailored to the video coding framework are presented to further improve the performance. To accommodate applications where decoder complexity is a cardinal concern, a block-constrained speed-up algorithm is also proposed. Experimental results show that the main approach and optimization methods yield significant coding gains across a diverse set of video sequences. Further experiments focus on the trade-off between performance and complexity, and demonstrate that the proposed speed-up algorithm offers complexity reduction by a large factor while maintaining most of the performance gains.
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Selecting among multiple transform kernels to code prediction residuals are widely used for better compression efficiency. Conventionally, the encoder performs trials of each transform to estimate the rate-distortion (R-D) cost. However such an exhaustive approach suffers from a significant increase of complexity due to the excessive trials. In this paper, a novel rate estimation approach is proposed to by-pass the entropy coding process for each transform type using the conditional Laplace distribution model. The proposed method estimates the Laplace distribution parameter by the context inferred by the quantization level and finds the expected rate of the coefficient for transform type selection. Furthermore, a greedy search algorithm for separable transforms is also presented to further accelerate the process. Experiment results show that transform type selection using the proposed rate estimation method achieves high accuracy at lower complexity.
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