Face Distortion Recovery Based on Online Learning Database for Conversational Video

Abstract

With the real-time requirement for video conversation, the coding system needs to adopt low delay and low complexity strategy to encode conversational videos, which may result in a significant decline of the video quality under the constrained bandwidth of network. In conversational videos, the face region attracts most human attentions. Therefore, recovering the distortion of face region will effectively improve the visual quality of conversational video. Actually, the participants in a conversation are usually unchanged in a relative long period, and similar facial expressions of the participants would be often repetitive. However, conventional video coding methods just consider the correlation of several neighboring frames while the long-range correlation of similar face regions in the whole conversational video has not been fully used. In this paper, we propose a face distortion recovery system to improve the visual quality of decoded conversational video by online learning an own face feature database for each user. First, at the sender side, the face feature database is established and online updated to include different facial expressions of the person. Then, at the receiver side, the low quality face regions in decoded video are recovered with the face patches in the database. Experimental results show that, under low bits rates the proposed method achieves average 5.22 dB gain with small burden to update the database.