Abstract

Perceptual Image Quality Assessment (IQA) has many applications. Existing IQA approaches typically work only for one of three scenarios: full-reference, non-reference, or reduced-reference. Techniques that attempt to incorporate image structure information often rely on hand-crafted features, making them difficult to be extended to handle different scenarios. On the other hand, objective metrics like Mean Square Error (MSE), while being easy to compute, are often deemed ineffective for measuring perceptual quality. This paper presents a novel approach to perceptual quality assessment by developing an MSE-like metric, which enjoys the benefit of MSE in terms of inexpensive computation and universal applicability while allowing structural information of an image being taken into consideration. The latter was achieved through introducing structure-preserving kernelization into a MSE-like formulation. We show that the method can lead to competitive FR-IQA results. Further, by developing a feature coding scheme based on this formulation, we extend the model to improve the performance of NR-IQA methods. We report extensive experiments illustrating the results from both our FR-IQA and NR-IQA algorithms with comparison to existing state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Multimedia and Expo, ICME 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781479970827
DOIs
StatePublished - Aug 4 2015
EventIEEE International Conference on Multimedia and Expo, ICME 2015 - Turin, Italy
Duration: Jun 29 2015Jul 3 2015

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2015-August
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

OtherIEEE International Conference on Multimedia and Expo, ICME 2015
CountryItaly
CityTurin
Period6/29/157/3/15

Keywords

  • Image Quality Assessment
  • Mean Square Error
  • kernel method

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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    Wang, Y., Zhang, Q., & Li, B. (2015). Structure-preserving Image Quality Assessment. In 2015 IEEE International Conference on Multimedia and Expo, ICME 2015 [7177436] (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2015-August). IEEE Computer Society. https://doi.org/10.1109/ICME.2015.7177436