Multiscale Natural Scene Statistical Analysis for No-Reference Quality Evaluation of DIBR-Synthesized Views

Ke Gu, Junfei Qiao, Sanghoon Lee, Hantao Liu, Weisi Lin, Patrick Le Callet

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


This paper proposes to blindly evaluate the quality of images synthesized via a depth image-based rendering (DIBR) procedure. As a significant branch of virtual reality (VR), superior DIBR techniques provide free viewpoints in many real applications, including remote surveillance and education; however, limited efforts have been made to measure the performance of DIBR techniques, or equivalently the quality of DIBR-synthesized views, especially in the condition when references are unavailable. To achieve this aim, we develop a novel blind image quality assessment (IQA) method via multiscale natural scene statistical analysis (MNSS). The design principle of our proposed MNSS metric is based on two new natural scene statistics (NSS) models specific to the DBIR-synthesized IQA. First, the DIBR-introduced geometric distortions damage the local self-similarity characteristic of natural images, and the damage degrees of self-similarity present particular variations at different scales. Systematically combining the measurements of the variations mentioned above can gauge the naturalness of the input image and thus indirectly reflect the quality changes of images generated using different DIBR methods. Second, it was found that the degradations in main structures of natural images at different scales remain almost the same, whereas the statistical regularity is destroyed in the DIBR-synthesized views. Estimating the deviation of degradations in main structures at different scales between one DIBR-synthesized image and the statistical model, which is constructed based on a large number of natural images, can quantify how a DIBR method damages the main structures and thus infer the image quality. Via trials, the two NSS-based features extracted above can well predict the quality of DIBR-synthesized images. Further, the two features come from distinct points of view, and we hence integrate them via a straightforward multiplication to derive the proposed blind MNSS metric, which achieves better performance than each component and state-of-the-art quality methods.

Original languageEnglish
Article number8704300
Pages (from-to)127-139
Number of pages13
JournalIEEE Transactions on Broadcasting
Issue number1
Publication statusPublished - 2020 Mar

Bibliographical note

Funding Information:
Manuscript received November 15, 2018; revised January 31, 2019; accepted February 7, 2019. Date of publication May 1, 2019; date of current version March 4, 2020. This work was supported in part by the National Science Foundation of China under Grant 61703009 and Grant 61890930-5, in part by the China Association for Science and Technology through the Young Elite Scientist Sponsorship Program under Grant 2017QNRC001, in part by the Beijing Excellent Talents Funding through the Young Top-Notch Talents Team Program under Grant 2017000026833ZK40, in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, and in part by the National Key Research and Development Project under Grant 2018YFC1900800-5. (Corresponding author: Ke Gu.) K. Gu and J. Qiao are with the Beijing Advanced Innovation Center for Future Internet Technology, Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China (e-mail:;

Publisher Copyright:
© 1963-12012 IEEE.

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering


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