Blind sharpness prediction based on image-based motion blur analysis

Taegeun Oh, Sanghoon Lee

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


For high bit rate video, it is important to acquire the video contents with high resolution, the quality of which may be degraded due to the motion blur from the movement of an object(s) or the camera. However, conventional sharpness assessments are designed to find focal blur caused either by defocusing or by compression distortion targeted for low bit rates. To overcome this limitation, we present a no-reference framework of a visual sharpness assessment (VSA) for high-resolution video based on the motion and scene classification. In the proposed framework, the accuracy of the sharpness estimation can be improved via pooling weighted by the visual perception from the object and camera movements and by the strong influence from the region with the highest sharpness. Based on the motion blur characteristics, the variance and the contrast over the spectral domain are used to quantify the perceived sharpness. Moreover, for the VSA, we extract the highly influential sharper regions and emphasize them by utilizing the scene adaptive pooling. Based on the subjective results, we demonstrate that the VSA can measure the video sharpness more accurately than other sharpness measurements for high-resolution video.

Original languageEnglish
Article number7039197
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Broadcasting
Issue number1
Publication statusPublished - 2015 Mar 1

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Blind sharpness prediction based on image-based motion blur analysis'. Together they form a unique fingerprint.

Cite this