Detail publikace

Video Quality Assessment in Experimental Retinal Imaging

Originální název

Video Quality Assessment in Experimental Retinal Imaging

Anglický název

Video Quality Assessment in Experimental Retinal Imaging

Jazyk

en

Originální abstrakt

This paper is focused on video quality assessment of experimental retinal video image sequences with two different approaches (automatic and expert analysis). Methods utilizing both video quality evaluations are presented and described within the paper. Evaluations consider the most frequent retinal imaging artefacts (contrast distortion, noise and blurriness). Automatic evaluating algorithm estimates the image quality based on three different parameters characterizing the image over time (signal to noise ratio - SNR, blurriness quality evaluation - BQE and contrast quality evaluation - CQE). Four different experts performed the evaluation with their subjective image perceiving and assess the degree of noise, blurriness and contrast in the images over time. The reliability of proposed algorithm for automatic retinal image video quality assessment seems to be correct for all three parameters. Medians of SNR as well as the medians of CQE are decreasing with decreasing image quality evaluated with 4 experts, and medians of BQE are increasing.

Anglický abstrakt

This paper is focused on video quality assessment of experimental retinal video image sequences with two different approaches (automatic and expert analysis). Methods utilizing both video quality evaluations are presented and described within the paper. Evaluations consider the most frequent retinal imaging artefacts (contrast distortion, noise and blurriness). Automatic evaluating algorithm estimates the image quality based on three different parameters characterizing the image over time (signal to noise ratio - SNR, blurriness quality evaluation - BQE and contrast quality evaluation - CQE). Four different experts performed the evaluation with their subjective image perceiving and assess the degree of noise, blurriness and contrast in the images over time. The reliability of proposed algorithm for automatic retinal image video quality assessment seems to be correct for all three parameters. Medians of SNR as well as the medians of CQE are decreasing with decreasing image quality evaluated with 4 experts, and medians of BQE are increasing.

BibTex


@inproceedings{BUT127623,
  author="Ivana {Labounková} and Radim {Kolář}",
  title="Video Quality Assessment in Experimental Retinal Imaging",
  annote="This paper is focused on video quality assessment of experimental retinal video image sequences with two different approaches (automatic and expert analysis). Methods utilizing both video quality evaluations are presented and described within the paper. Evaluations consider the most frequent retinal imaging artefacts (contrast distortion, noise and blurriness). Automatic evaluating algorithm estimates the image quality based on three different parameters characterizing the image over time (signal to noise ratio - SNR, blurriness quality evaluation - BQE and contrast quality evaluation - CQE). Four different experts performed the evaluation with their subjective image perceiving and assess the degree of noise, blurriness and contrast in the images over time. The reliability of proposed algorithm for automatic retinal image video quality assessment seems to be correct for all three parameters. Medians of SNR as well as the medians of CQE are decreasing with decreasing image quality evaluated with 4 experts, and medians of BQE are increasing.",
  address="Brno University of Technology",
  booktitle="Sborník příspěvků studentské konference Blansko",
  chapter="127623",
  edition="1",
  howpublished="electronic, physical medium",
  institution="Brno University of Technology",
  year="2016",
  month="august",
  pages="51--54",
  publisher="Brno University of Technology",
  type="conference paper"
}