05 visual quality assessment

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05 visual quality assessment

  1. 1. Visual Quality Assessment T.L.R. Mengko
  2. 2. Bad vs Good Potato
  3. 3. Visual Quality Measurement• Measurement of visual quality is of fundamental importance to numerous image and video processing applications.• The goal of quality assessment (QA) to automatically assess the quality of images or videos in a perceptually consistent manner.• Image QA algorithms generally interpret image quality as fidelity or similarity with a ‘reference’ or ‘perfect’ image in some perceptual space.• Such ‘Full-Reference’ QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psycho-visual features of the human visual system (HVS), or by signal fidelity measures.
  4. 4. Describing Existing Visual Resources
  5. 5. Some Parameters• mean-squared-error MSE• peak signal-to-noise-ratio PSNR• structural similarity index SSIM• multi-scale SSIM index MSSIM• visual signal-to-noise ratio VSNR• visual information fidelity VIF• pixel-based VIF VIFP• universal quality index UQI• information fidelity criterion IFC• noise quality measure NQM• weighted signal-to-noise ratio WSNR• signal-to-noise ratio SNR

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