Objective Video Quality Assessment Based on Perceptually Weighted Mean Squared Error

Sudeng HU, Lina JIN, Hanli WANG, Yun ZHANG, Sam KWONG, C.-C. Jay KUO

Research output: Journal PublicationsJournal Article (refereed)peer-review

38 Citations (Scopus)


Object quality assessment for compressed video is critical to various video compression systems that are essential in the video delivery and storage. Although mean squared error (MSE) is computationally simple, it may not be accurate to reflect the perceptual quality of compressed videos, which are also affected dramatically by the characteristics of the human visual system (HVS), such as contrast sensitivity, visual attention, and masking effect. In this paper, a video quality metric is proposed based on perceptually weighted MSE. A low-pass filter is designed to model the contrast sensitivity of the HVS with the consideration of visual attention. The imperceptible distortion is adaptively removed in the salient and nonsalient regions. To quantitatively measure the masking effect, the randomness of video content is proposed in both the spatial and temporal domains. Since the masking effect highly depends on the regularity of structure and motion in the spatial and temporal directions, the video signal is modeled as a linear dynamic system, and the prediction error of future frames from previous frames is used as randomness to measure the significance of masking. The relation is investigated between MSE and perceptual quality scores across various contents, and a masking modulation model is proposed to compensate the impact of the masking effect on the MSE. The performance of the proposed quality metric is validated on three video databases with various compression distortions. The experimental results demonstrate that the proposed algorithm outperforms other benchmark quality metrics.
Original languageEnglish
Pages (from-to)1844-1855
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number9
Early online date20 Apr 2016
Publication statusPublished - Sept 2017
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61472281 and Grant 61471348; in part by the Shu Guang Project of Shanghai Municipal Education Commission through the Shanghai Education Development Foundation under Grant 12SG23; in part by the Program for Professor of Special Appointment (Eastern Scholar) within the Shanghai Institutions of Higher Learning under Grant GZ2015005; in part by the Shenzhen Overseas High-Caliber Personnel Innovation and Entrepreneurship Project under Grant KQCX20140520154115027; and in part by the Guangdong Special Support Program for Youth Science and Technology Innovation Talents under Grant 2014TQ01X345.


  • Human visual system (HVS)
  • low-pass filter
  • masking effect
  • video quality assessment
  • visual attention


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