Face location in wavelet-based video compression for high perceptual quality videoconferencing

We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an adaptive quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing, since the face is often the most important part and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximate face mask is then used in the quantization of the decomposed subbands. At the same total bit rate, coarser quantization of the background enables the face region to be quantized finer and coded with a higher quality. Moreover, the resultant larger quantization noise in the background can be suppressed using an edge-preserving enhancement algorithm. Experimental results have shown that the perceptual image quality is greatly improved using the proposed scheme