Abstract

How accurately are people able to use the absolute category rating (ACR) 5-level scale? Put another way, how repeatable are an individual subject's scores? Several subjective experiments have asked subjects to rate the same sequences a couple of times. Analyses indicate that none of the subjects exactly repeated their prior scores for these sequences. We would like to better understand this imperfection. This paper uses ACR subjective video quality tests to explore the precision of subjective ratings. To make formal measurements possible, we propose a theoretical subject model that is the main contribution of this paper. The proposed subject model indicates three major factors that influence accuracy: subject bias, subject inaccuracy, and stimulus scoring difficulty. These appear to be separate random effects and their existence is a reason why none of the subjects were able to perfectly repeat scores. There are three key consequences. First, subject scoring behavior includes a random component that spans approximately half of the rating scale. Second, the sensitivity and accuracy of most subjective analyses can be improved if the subject scores are normalized by removing subject bias. Third, to some extent, multiple subjects can be replaced with a single subject who rates each sequence multiple times.

Highlights

  • S UBJECTIVE experiments are key tools that link technical solutions with human perception

  • We look for direct evidence supporting our subject model by applying these formulae to subjective datasets, and look for indirect proof by examining behaviors predicted by our subject model

  • First is a collection of six high definition television (HDTV) experiments conducted by the Video Quality Experts Group (VQEG) to validate HDTV objective quality metrics

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Summary

INTRODUCTION

S UBJECTIVE experiments are key tools that link technical solutions with human perception. The collected answers are used to reach conclusions and make product development decisions

Motivation
Contribution
RELATED WORK
DATASETS
Other Datasets
SUBJECT SCORING MODEL
DISCRETE MODEL ANALYSIS
CONTINUOUS MODEL ANALYSIS
Analysis of
Estimating Model Parameters
Model Correctness by Normalizing Subjective Data
VIII. CONCLUSION
Findings
FUTURE WORK
Full Text
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