Human centered peceptual video compression

In traditional visual saliency based video compression, the saliency feature changes according to persons, viewpoints, and distances. In this paper, we propose to apply a technique of human centered perceptual computation to improve video coding in the region of human centered perception. To detect the region of interest (ROI), we construct Harr and histogram of oriented gradients (HOG) features based combo of detectors to analyze a video in the first frame (intra-frame). The optical flow in human centered ROI is then used for macroblock (MB) quantization adjustment in H.264/AVC. For each MB, the quantization parameter (QP) is optimized with density value of optical flow image. The QP optimization process is based on a MB mapping model, which can be calculated by an inverse of the inverse tangent function. The Lagrange multiplier in the rate distortion optimization is also adapted so that the MB distortion at human centered region is minimized. By evaluating our scheme with the H.264 reference software, our results show that the proposed algorithm can improve the visual quality of ROI by about 1.01 dB while preserving coding efficiency.