Compressed Image Quality Metric Based on Perceptually Weighted Distortion

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

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

36 Citations (Scopus)

Abstract

Objective quality assessment for compressed images is critical to various image compression systems that are essential in image delivery and storage. Although the mean squared error (MSE) is computationally simple, it may not be accurate to reflect the perceptual quality of compressed images, which is also affected dramatically by the characteristics of human visual system (HVS), such as masking effect. In this paper, an image quality metric (IQM) is proposed based on perceptually weighted distortion in terms of the MSE. To capture the characteristics of HVS, a randomness map is proposed to measure the masking effect and a preprocessing scheme is proposed to simulate the processing that occurs in the initial part of HVS. Since the masking effect highly depends on the structural randomness, the prediction error from neighborhood with a statistical model is used to measure the significance of masking. Meanwhile, the imperceptible signal with high frequency could be removed by preprocessing with low-pass filters. The relation is investigated between the distortions before and after masking effect, and a masking modulation model is proposed to simulate the masking effect after preprocessing. The performance of the proposed IQM is validated on six image databases with various compression distortions. The experimental results show that the proposed algorithm outperforms other benchmark IQMs.
Original languageEnglish
Pages (from-to)5594-5608
JournalIEEE Transactions on Image Processing
Volume24
Issue number12
DOIs
Publication statusPublished - 1 Dec 2015
Externally publishedYes

Keywords

  • compressed image
  • human visual system
  • Image quality assessment
  • low-pass filter
  • masking effect

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