- The paper presents FRIQUEE, a novel method that predicts image quality using a bag-of-features approach on authentically distorted images.
- It employs divisive normalization and multi-domain transformations across color spaces to extract quality-aware features that mirror human visual perception.
- The model significantly outperforms traditional IQA methods on the LIVE In the Wild dataset, demonstrating superior correlation with human opinion scores.
Perceptual Quality Prediction on Authentically Distorted Images
The paper by Ghadiyaram and Bovik presents a novel approach for predicting the perceptual quality of authentically distorted images, which often contain complex, composite distortions. Traditional image quality assessment (IQA) models tend to focus on synthetically distorted images that do not adequately reflect the comprehensive distortion types encountered in real-world scenarios. The authors propose a robust framework utilizing a bag of features approach that leverages natural scene statistics (NSS) across various perceptual color spaces and transform domains to evaluate image quality.
The main contribution of the paper is the introduction of a perceptually motivated bag of features method designed to circumvent the limitations of existing top-performing blind IQA models which primarily handle inauthentic distortions. This approach extracts a broad array of quality-aware features from real-world authentically distorted images, enabling the training of a regressor to predict image quality in a way that closely aligns with human visual judgment. Utilizing a large dataset containing authentically distorted images, the paper emphasizes the pivotal differences between synthetic and authentic distortions, highlighting the destabilization of statistical regularities assumed by prior models.
Among the methodological innovations, the paper proposes several new statistical feature maps that enhance the discriminative power of the model. These include divisive normalization techniques across multiple color spaces (RGB, CIELAB, and LMS) and transformations such as the steerable pyramid wavelet transform. These transformations are theorized to model processes occurring in the human visual system, particularly in the retina and cortex.
Numerically, the proposed model, named FRIQUEE (Feature Maps based Referenceless Image QUality Evaluation Engine), demonstrates significant improvements in correlation with human opinion scores over existing methods when tested against the LIVE In the Wild Image Quality Challenge Database. The model achieves superior prediction accuracy compared to other blind IQA models like BRISQUE, DIIVINE, and BLIINDS-II, illustrating its robustness in handling real-world distortions.
Furthermore, the paper carefully outlines the implications of utilizing authentic distortion datasets, underscoring the necessity for IQA models to integrate features that capture more diverse distortion characteristics. It postulates that further investigation into authentic distortions will be instrumental in developing more robust quality predictors. The research also anticipates implications for practical applications, such as enhancing consumer devices' image capture and processing capabilities.
In terms of future work, the authors suggest extending their methodology to video quality assessment, leveraging the framework developed for static images to accommodate temporal dynamics present in video data. Additionally, the paper invites exploration into practical implementations that optimize image quality directly within digital cameras and mobile devices.
Overall, this work represents a noteworthy progression in the domain of perceptual image quality assessment, providing a comprehensive framework that significantly enhances the capability of predicting the quality of authentically distorted images—those most representative of common user experiences.