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This study created two image databases (MMSP-FaceA and MMSP-FaceB) to assess how emotions like happiness and sadness affect perceived image quality under JPEG compression. Results provide insights into affective image quality perception.

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Area of Science:

  • Computer Vision
  • Psychophysics
  • Image Processing
  • Affective Computing

Background:

  • Assessing image quality is crucial in digital media.
  • The influence of affective content on image quality perception requires further investigation.
  • Existing image quality databases often lack controlled affective stimuli.

Purpose of the Study:

  • To evaluate the role of affective content (happy vs. sad faces) in subjective image quality assessment.
  • To create and validate two novel image databases (MMSP-FaceA and MMSP-FaceB) for studying affective image quality.
  • To analyze the impact of JPEG compression levels on the perception of emotionally charged facial images.

Main Methods:

  • Generation of two image databases (MMSP-FaceA and MMSP-FaceB) comprising 230 images each.
  • Databases include close-ups of happy and sad faces at five quality levels (including pristine) using JPEG compression (q-factors 10, 15, 20, 30).
  • Conducting psychophysical experiments via a web-based interface where participants rated image quality on a five-point scale.

Main Results:

  • The MMSP-FaceA database contains JPEG-compressed images with original backgrounds.
  • The MMSP-FaceB database consists of images with backgrounds removed to isolate affective content influence.
  • Subjective quality scores from psychophysical experiments were collected for both databases.

Conclusions:

  • The developed MMSP-FaceA and MMSP-FaceB databases provide valuable resources for research on affective image quality.
  • These databases enable the study of how emotional expressions interact with technical image quality parameters.
  • The collected subjective scores offer empirical data for developing more perceptually relevant image quality models.