Abstract
To find good trade-offs between the rate-distortion efficiency of motion-compensated, discrete-cosine-transform video coding and its computational complexity, we explore a scheme which uses an adaptive displacement vector field for motion compensation. The sampling density of the field is nonuniform and is adapted to the image scene using a Lagrangian optimization algorithm. The algorithm is formulated to minimize an overall distortion for a given bit rate constraint on the video frame. The algorithm takes into account the contributions to the overall bit rate and distortion due to motion vector and prediction residual coding. Our simulation results demonstrate that, for various common video sequences, the proposed scheme outperforms a popular H.263 test model by about 0.5 dB of peak signal-to-noise ratio. Significant reduction in coding artifacts is observed, and visual quality improvement is highly palpable. The work demonstrates that a suitably crafted rate-distortion optimization scheme can improve performance without exacerbating complexity.
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