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No-reference image sharpness assessment in autoregressive parameter space.

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  • 1Shanghai Key Laboratory of Digital Media Processing and Transmissions, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China. gukesjtuee@gmail.com

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We developed a novel blind sharpness metric using autoregressive model parameters. This method accurately predicts image sharpness across luminance and color domains, outperforming existing algorithms.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Perceptual Quality Assessment

Background:

  • Assessing image sharpness is crucial for image quality evaluation.
  • Existing no-reference (NR) methods often struggle with accuracy and efficiency.
  • Blind image quality assessment requires robust metrics that do not rely on pristine references.

Purpose of the Study:

  • To propose a novel no-reference (NR) image sharpness metric.
  • To leverage autoregressive (AR) model parameters for sharpness estimation.
  • To extend the metric to incorporate color information and stereoscopic images.

Main Methods:

  • Analysis of AR model parameters to calculate energy and contrast differences.
  • Pointwise estimation of AR coefficients and percentile pooling for overall sharpness score.
  • Extension of the model to the YIQ color space and application to stereoscopic images using binocular rivalry.

Main Results:

  • The proposed metric demonstrates superior performance compared to existing NR algorithms and state-of-the-art estimators.
  • Validation on multiple large-scale image databases (LIVE, TID2008, CSIQ, TID2013) confirms efficiency.
  • High performance achieved on stereoscopic image databases (LIVE3D-I, LIVE3D-II).

Conclusions:

  • The developed NR sharpness metric is effective and efficient.
  • Incorporating color information enhances perceptual relevance.
  • The metric shows promise for advanced applications like stereoscopic image quality assessment.