Packet-Layer Quality Assessment for Networked Video

  • Honglei Su State Key Laboratory of Integrated Service Networks Xidian University, Xi’an, Shaanxi 710071, China
  • Fuzheng Yang State Key Laboratory of Integrated Service Networks Xidian University, Xi’an, Shaanxi 710071, China
  • Jiarun Song State Key Laboratory of Integrated Service Networks Xidian University, Xi’an, Shaanxi 710071, China

Abstract

To realize real-time and non-intrusive quality monitoring for networked video, a content-adaptive packet-layer model for quality assessment is proposed. Considering the fact that the coding distortion of a video is dependent not only on the bit-rate but also on the motion characteristic of the video content, temporal complexity is evaluated and incorporated in quality assessment in the proposed model. Since very limited information is available for a packet-layer model, an adaptive method for frame type detection is first applied. Then the temporal complexity which reflects the motion characteristic of the video content is estimated using the ratio of the bit-rate for coding I frames and P frames. The estimated temporal complexity is incorporated in the proposed model, making it adaptive to different video content. Experimental results show that the proposed model achieves an advanced performance in comparison with the ITU-T G.1070 model.

Author Biography

Honglei Su, State Key Laboratory of Integrated Service Networks Xidian University, Xi’an, Shaanxi 710071, China
Department of Mathematics and Computer Science

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Published
2014-09-18
How to Cite
SU, Honglei; YANG, Fuzheng; SONG, Jiarun. Packet-Layer Quality Assessment for Networked Video. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 7, n. 3, p. 565-573, sep. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1396>. Date accessed: 05 july 2020. doi: https://doi.org/10.15837/ijccc.2012.3.1396.

Keywords

Packet-Layer Model, Networked Video, Video Quality Assessment, Coding Distortion, Temporal Complexity