Liao, X., Wei, X., Zhou, M., Li, Z., Kwong, S. (2024). Image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces. IEEE Transactions on Image Processing, 33, 4044–4059. https://doi.org/10.1109/tip.2024.3409176
Abstract:
This study aims to develop advanced and training free full-reference image quality assessment (FR-IQA) models based on deep neural networks. Specifically, we investigate measures that allow us to perceptually compare deep network features and reveal their underlying factors. We find that distribution measures enjoy advanced perceptual awareness and test the Wasserstein distance (WSD), Jensen-Shannon divergence (JSD),
and symmetric Kullback-Leibler divergence (SKLD) measures when comparing deep features acquired from various pretrained deep networks, including the Visual Geometry Group (VGG) network, SqueezeNet, MobileNet, and EfficientNet. The proposed FR-IQA models exhibit superior alignment with subjective human
evaluations across diverse image quality assessment (IQA) datasets without training, demonstrating the advanced perceptual relevance of distribution measures when comparing deep network features. Additionally, we explore the applicability of deep distribution measures in image super-resolution enhancement tasks, highlighting their potential for guiding perceptual enhancements. The code will be publicly available upon the acceptance of the manuscript.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done