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JEG-Hybrid: Automotive Image Quality

JEG-Hybrid: Automotive Image Quality. Anthony Winterlich Vladimir Zlokolica Edward Jones Martin Glavin Connaught Automotive Research Group Electrical & Electronic Engineering National University of Ireland, Galway. Current Applications. Junction View. Current Applications.

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JEG-Hybrid: Automotive Image Quality

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  1. JEG-Hybrid: Automotive Image Quality Anthony Winterlich Vladimir Zlokolica Edward Jones Martin Glavin Connaught Automotive Research Group Electrical & Electronic Engineering National University of Ireland, Galway

  2. Current Applications Junction View

  3. Current Applications Park Assist

  4. Subjective testing challenges • Fish-eye distortion • Sharpness • Visual Saliency • Colour balance and contrast (HDR scenes) • Noise in high & low light conditions

  5. LOWER SPATIAL RESOLUTION AT THE EDGE OF AN IMAGE RESULTS IN A LOSS OF PERCEPTUAL QUALITY

  6. LOWER SPATIAL RESOLUTION AT THE EDGE OF AN IMAGE RESULTS IN A LOSS OF PERCEPTUAL QUALITY

  7. FEATURE VECTORS ARE EXTRACTED TO DETERMINE SALIENCY

  8. HDR and contrast

  9. Quality in low light conditions

  10. Existing subjective testing standards • ITU-R Recommendation BT.500-11, “Methodology for the subjective assessment of the quality of television pictures,” • General viewing conditions for subjective assessments in laboratory environment • Not appropriate for automotive vision systems! • The absence of defects in the reference part of the presentation pair is crucial to obtain stable results. • Fish-eye images are not free of defects!

  11. Thank You! SSIM : 0.7134

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