May 2003
Volume 44, Issue 13
ARVO Annual Meeting Abstract  |   May 2003
Predicting Subjective Image Quality from the Eye’s Wave Aberration
Author Affiliations & Notes
  • L. Chen
    Center for Visual Science, University of Rochester, Rochester, NY, United States
  • J. Porter
    Center for Visual Science, University of Rochester, Rochester, NY, United States
  • B. Singer
    Center for Visual Science, University of Rochester, Rochester, NY, United States
  • L. Llorente
    Instituto de Óptica, CSIC, Madrid, Spain
  • L. Nagy
    Instituto de Óptica, CSIC, Madrid, Spain
  • D.R. Williams
    Instituto de Óptica, CSIC, Madrid, Spain
  • Footnotes
    Commercial Relationships  L. Chen, None; J. Porter, Bausch & Lomb C; B. Singer, Bausch & Lomb C; L. Llorente, None; L. Nagy, None; D.R. Williams, Bausch & Lomb F, C; University of Rochester P.
  • Footnotes
    Support  NIH Grant EY01319, grants from Bausch & Lomb and NSF Center for Adaptive Optics, AST-9876783
Investigative Ophthalmology & Visual Science May 2003, Vol.44, 2121. doi:
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    • Get Citation

      L. Chen, J. Porter, B. Singer, L. Llorente, L. Nagy, D.R. Williams; Predicting Subjective Image Quality from the Eye’s Wave Aberration . Invest. Ophthalmol. Vis. Sci. 2003;44(13):2121.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract: : Purpose: Despite the proliferation of wavefront sensing to characterize the optical quality of individual eyes, there is not yet an accurate way to determine from a wave aberration how severely it will impact the patient’s vision. The most commonly used metric, RMS wavefront error, predicts subjective image quality poorly. Our goal is to establish the best metric for predicting subjective blur from the wave aberration. Methods: We used the deformable mirror in the Rochester Adaptive Optics Ophthalmoscope to remove the aberrations from the subject’s eye and to replace them with the wave aberrations of other eyes that had previously been measured with a wavefront sensor. By allowing single individuals to evaluate the subjective impact of the optics of many different eyes, we control for individual differences in neural processing. We chose wave aberrations from the eyes of 9 post-LASIK patients. The visual stimulus was high contrast, containing many sharp edges at all orientations. The subject viewed this stimulus, blurred by one of the post-LASIK wave aberrations, for 500 msec. This test period was immediately followed by a matching period, also of 500 msec, during which the same stimulus was blurred by a wave aberration consisting only of a variable amount of defocus. The subject adjusted the defocus to match the subjective blur seen with the post-LASIK wave aberration. Test and matching periods alternated until a satisfactory match was made. This procedure was repeated 5 times for each of the 9 wave aberrations. We then determined which among many image quality metrics best predicted these matching data. Results: RMS wavefront error was a poor predictor of the data, as was the Strehl ratio, a common metric used in optics. The sharpness metric that does the best so far at predicting how much different wave aberrations blur vision is the integral of the product of the eye’s point spread function and a Gaussian function with a standard deviation of ~1 minute of arc. Conclusions: Our neural sharpness metric is effective at describing the subjective sharpness of images viewed with the wave aberrations of real eyes. The metric is biologically-plausible, is fast to compute, does not require the interpretation of individual Zernike modes, and can be incorporated in the software of current wavefront sensors. This metric will provide clinicians with a single number that describes the subjective impact of each patient’s wave aberration and will also increase the accuracy of refraction estimates from wavefront-based autorefractors and phoropters.

Keywords: physiological optics • refractive surgery • spatial vision 

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