A Video Image Compression Method based on Visually Salient Features

摘要

This study presents a visual attention model for determining image areas to receive different extents of video compression in order to minimize perceived program artefacts whilst maximizing the compression possible. The model integrates features related to motion with existing video image compression algorithms. The proposed visual attention model extracts the color, intensity, textural, and motion features of a video to determine the predicted region of interest (ROI). First, color, intensity, and texture saliency maps are generated by applying the "center-surround" method and a motion saliency map is produced using a difference operator. Then, a multi-channel weighting method is used to generate a global saliency map and to determine the ROI according to a winner-takes-all network (WTA). The proposed video image compression algorithm performs either low or no compression on the ROI while a high degree of compression is applied to the other regions. Tests indicate that the proposed visual attention model is able swiftly to identify the ROI, allowing the proposed compression algorithm to exert a high compression efficiency yet with minimally noticeable visual degradation.