Performance and computational complexity optimization techniques in configurable video coding system.

摘要

In order to achieve high performance in terms of compression ratio, nest standard video coders have a high computational complexity. Motion estimation in sub-pixel accuracy and in model-based rate distortion optimization is approached from a practical implementation perspective; then, a configurable coding scheme is proposed and analyzed with respect to computational complexity and distortion. The proposed coding scheme consists of three coding modules: motion estimation, sub-pixel accuracy, and DCT pruning, and their control variables can take several values, leading to a significantly different coding performance.; The major coding modules are analyzed in terms of computational complexity and distortion (C-D) in the H.263 video coding framework. Based on the analyzed data, operational C-D curves are obtained through an exhaustive search and the Lagrangian multiplier method. The proposed scheme has a deterministic feature that satisfies the given computational constraint, regardless of the changing properties of the input video sequence. It is shown that, in terms of PSNR, an optimally chosen operational mode makes a significant difference compared to non-optimal modes. Furthermore, an adaptive scheme iteratively controlling the optimal coding mode is introduced and compared with the fixed scheme, whose operating mode is determined based on the rate distortion model parameters obtained by pre-processing off-line.; To evaluate the performance of proposed scheme according to input video sequences, we apply video sequences other than those involved in the process of model parameter estimation, and show that the model parameters are accurate enough to be applied, regardless of the type of input video sequences. Experimental results demonstrate that, in the adaptive approach, computation reductions of up to 19% are obtained in test video sequences compared to the fixed, while the degradations of the reconstructed video are less than 0.05dB. In addition, the adaptive approach is proven to be more effective with active video sequences than with silent video sequences.