Blind image quality assessment using joint statistics of gradient magnitude and laplacian features

Wufeng Xue, Xuanqin Mou, Lei Zhang, Alan C. Bovik, Xiangchu Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

336 Citations (Scopus)

Abstract

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.
Original languageEnglish
Article number6894197
Pages (from-to)4850-4862
Number of pages13
JournalIEEE Transactions on Image Processing
Volume23
Issue number11
DOIs
Publication statusPublished - 1 Nov 2014

Keywords

  • Blind image quality assessment
  • gradient magnitude
  • jointly adaptive normalization
  • LOG

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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