Efficient Calibration of Single Opinion Scores for Accurate Image Quality Assessment

Core Concepts

The proposed perceptual constancy constrained calibration (PC3) method can efficiently estimate the mean opinion score (MOS) from a single opinion score (SOS), significantly reducing the cost of MOS collection while improving the performance of image quality assessment (IQA) models.

Abstract

The paper proposes a method called perceptual constancy constrained calibration (PC3) to efficiently estimate the mean opinion score (MOS) from a single opinion score (SOS) for image quality assessment (IQA). The key insights are: Modeling the SOS as a normal distribution with the unknown MOS as its expectation, the MOS estimation is formulated as a maximum likelihood estimation problem. Considering the perceptual correlation of pairwise images, the likelihood of SOS is represented by the joint conditional probability of the current image and a randomly selected reference image. A learnable relative quality measure is introduced to predict the MOS difference between two images, and the current image's MOS is estimated as the sum of the reference image's MOS and their relative quality. The relative quality measure and the current image's MOS are optimized alternatively via backpropagation and Newton's method, respectively, under the perceptual constancy constraint that the MOS should remain unchanged regardless of the reference image. Experiments on four popular IQA datasets show that the proposed PC3 method is effective in calibrating biased SOSs and significantly improves the performance of IQA models when only SOSs are available for training.

Stats

The proposed method significantly improves the SRCC, PLCC, and MSE performance compared to using the raw SOS across all tested datasets. The performance improvement of PC3 is more significant when the bias rate of SOS is higher. Applying PC3 to calibrate the SOS can significantly improve the performance of the IQA model NIMA, reducing the performance degradation caused by using only SOS.

Quotes

"The proposed method is also robust to different distortion types, whose performance improvements are similar to the synthetic distortion in TID, VCL, and the authentic distortions in LIVEC, KONIQ." "The proposed method is also efficient for calibrating the mixed subjective labels, whose performances are superior to the SOS. Meanwhile, we can find that the performance improvement of PC3 is more significant for a high bias rate."

Key Insights Distilled From

Perceptual Constancy Constrained Single Opinion Score Calibration for Image Quality Assessment

by Lei Wang,Des... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19595.pdf

Deeper Inquiries

How can the proposed PC3 method be extended to handle other types of subjective quality labels beyond SOS, such as pairwise comparisons or ranking

The PC3 method can be extended to handle other types of subjective quality labels beyond SOS by adapting the calibration framework to accommodate different types of subjective annotations. For pairwise comparisons, the perceptual constancy constraint can be modified to consider the relative quality between pairs of images instead of individual images. This adjustment would involve modeling the likelihood of pairwise comparisons and updating the estimated quality scores based on the perceptual correlation between image pairs. By incorporating the pairwise comparison information into the calibration process, the PC3 method can effectively handle this type of subjective quality label. Similarly, for ranking-based subjective quality labels, the PC3 method can be extended by incorporating the ranking information into the calibration framework. This extension would involve developing a mechanism to capture the relative quality order among images based on the ranking provided by human annotators. By integrating the ranking information into the calibration process, the PC3 method can adapt to handle ranking-based subjective quality labels and improve the accuracy of the estimated quality scores.

What are the potential limitations of the perceptual constancy constraint, and how could it be relaxed or generalized to further improve the calibration performance

The perceptual constancy constraint, while effective in ensuring the stability of the calibration process, may have limitations in scenarios where the assumption of unchanged quality across different reference images does not hold. In cases where the quality of an image can vary significantly based on the reference image chosen, the constraint may lead to suboptimal calibration results. To address this limitation and enhance the calibration performance, the perceptual constancy constraint could be relaxed or generalized in the following ways: Adaptive Reference Selection: Instead of assuming a fixed reference image for each calibration iteration, an adaptive reference selection mechanism could be introduced. This mechanism would dynamically choose the reference image based on the characteristics of the current image, allowing for more flexible calibration that accounts for varying quality perceptions. Quality Context Modeling: Incorporating contextual information about the images and their relationships could help in relaxing the perceptual constancy constraint. By considering the context in which images are viewed or compared, the calibration process can adapt to different scenarios and improve the accuracy of the estimated quality scores. Hierarchical Constraint Relaxation: Introducing a hierarchical constraint relaxation approach where the constancy constraint is gradually relaxed as the calibration progresses could provide a balance between stability and adaptability. This approach would allow for a gradual adjustment of the constraint based on the calibration performance, ensuring optimal results. By exploring these strategies to relax or generalize the perceptual constancy constraint, the PC3 method can overcome potential limitations and further enhance its calibration performance in handling subjective quality labels.

Given the success of PC3 in improving IQA model learning, how could the insights from this work be applied to enhance other computer vision tasks that rely on subjective human annotations

The insights gained from the success of the PC3 method in improving IQA model learning can be applied to enhance other computer vision tasks that rely on subjective human annotations by leveraging similar calibration techniques. Some ways to apply these insights include: Subjective Label Refinement: Apply the calibration framework of PC3 to refine subjective annotations in tasks such as object detection, semantic segmentation, or image captioning. By calibrating noisy or biased human annotations, the performance of models trained on subjective labels can be significantly improved. Multi-Task Learning: Extend the PC3 approach to handle multi-task learning scenarios where multiple subjective quality labels are available for different tasks. By jointly calibrating subjective annotations across tasks, a unified model can be trained to leverage the collective knowledge from diverse human annotations. Transfer Learning: Utilize the principles of PC3 for transfer learning tasks where subjective annotations from one domain are used to train models in another domain. By calibrating the transfer labels to align with the target domain's characteristics, the transfer learning process can be enhanced, leading to improved model generalization. By applying the insights from PC3 to other computer vision tasks, researchers can enhance the reliability and effectiveness of models trained on subjective human annotations, ultimately improving the performance of various vision tasks.