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Related Experiment Videos

Optimizing multiscale SSIM for compression via MLDS.

Christophe Charrier1, Kenneth Knoblauch, Laurence T Maloney

  • 1Université de Caen-Basse Normandie, GREYC Laboratory, Equipe Image, ENSICAEN, Caen 14050, France. christophe.charrier@unicaen.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 8, 2012
PubMed
Summary

Maximum Likelihood Difference Scaling (MLDS) offers a robust method for evaluating image compression quality. This approach refines image quality assessment algorithms like MS-SSIM by quantifying perceptual differences more accurately.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Perceptual Quality Assessment

Background:

  • Assessing perceived quality of compressed images is vital for evaluating compression methods.
  • Traditional methods rely on subjective observer ratings, which have limitations like inter-observer variability and overlooking specific artifacts.
  • Existing image quality assessment algorithms, such as multiscale structural similarity (MS-SSIM), are often calibrated using these subjective ratings.

Purpose of the Study:

  • To introduce and apply Maximum Likelihood Difference Scaling (MLDS) for a more objective assessment of perceived image quality.
  • To evaluate the performance of the MS-SSIM algorithm using the MLDS method.
  • To demonstrate how MLDS data can be used to recalibrate and improve the performance of image quality assessment algorithms.

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Main Methods:

  • Utilized Maximum Likelihood Difference Scaling (MLDS) to quantify supra-threshold perceptual differences between image pairs.
  • Applied MLDS to assess perceived image quality across a wide range of images and compression rates.
  • Analyzed the performance of the multiscale structural similarity (MS-SSIM) algorithm in conjunction with MLDS data.

Main Results:

  • MLDS provides a quantitative measure of perceptual differences, overcoming subjectivity in traditional rating scales.
  • The study examined how perceived image quality, as estimated by MLDS, varies with increasing compression levels.
  • Data derived from MLDS enabled the recalibration of the MS-SSIM algorithm, leading to improved performance.

Conclusions:

  • MLDS is a powerful tool for assessing perceived image quality and understanding compression artifact perception.
  • The MLDS approach circumvents the limitations of traditional subjective rating methods.
  • Recalibrating image quality assessment algorithms like MS-SSIM using MLDS data enhances their accuracy in predicting human perception.