Blind Sharpness Prediction for Ultrahigh-Definition Video Based on Human Visual Resolution

Haksub Kim, Jongyoo Kim, Taegeun Oh, Sanghoon Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)


We explore a no-reference sharpness assessment model for predicting the perceptual sharpness of ultrahigh-definition (UHD) videos through analysis of visual resolution variation in terms of viewing geometry and scene characteristics. The quality and sharpness of UHD videos are influenced by viewer perception of the spatial resolution afforded by the UHD display, which depends on viewing geometry parameters including display resolution, display size, and viewing distance. In addition, viewers may perceive different degrees of quality and sharpness according to the statistical behavior of the visual signals, such as the motion, texture, and edge, which vary over both spatial and temporal domains. The model also accounts for the resolution variation associated with fixation and foveal regions, which is another important factor affecting the sharpness prediction of UHD video over the spatial domain and which is caused by the nonuniform distribution of the photoreceptors. We calculate the transition of the visually salient statistical characteristics resulting from changing the display's screen size and resolution. Moreover, we calculated the temporal variation in sharpness over consecutive frames in order to evaluate the temporal sharpness perception of UHD video. We verify that the proposed model outperforms other sharpness models in both spatial and temporal sharpness assessments.

Original languageEnglish
Article number7373624
Pages (from-to)951-964
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number5
Publication statusPublished - 2017 May

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Blind Sharpness Prediction for Ultrahigh-Definition Video Based on Human Visual Resolution'. Together they form a unique fingerprint.

  • Cite this