Search

No-reference image quality assessment (NR-IQA), which aims to estimate the perceptual quality of a degraded image without accessing the corresponding original image, is a key challenge in low-level computer vision. Recent advances in deep learning have enabled the development of high-performance NR-IQA methods. However, such methods are limited, as they are highly dependent on the training dataset. Recognizing this limitation and avoiding task-specific training, an alternative method has been proposed that employ pre-trained visual language models for zero-shot NR-IQA; however, this approach does not provide any basis for decision-making and is not explainable. In this study, we propose ZEN-IQA, a new zero-shot and explainable NR-IQA method. Utilizing a new approach involving carefully constructed prompt pairs and triplets makes the evaluation process more intuitive and easier to understand. Our comparative analysis reveals that ZEN-IQA not only has high interpretability, but also outperforms methods using handcrafted features and state-of-the-art deep learning methods trained based on datasets that differ from the test set. We also applied ZEN-IQA to images before and after image processing and conducted experiments to evaluate how perceptual quality changes with image processing. The code is publicly available at https://github.com/mtakamichi/ZEN-IQA.

Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance. On the other hand, a number of color extensions of NLSS-based grayscale image restoration methods have been developed. These extensions focus on the pixel-wise correlation among color channels. However, a natural color image also has a complex dependency, known as an inter-channel dependency, among local regions from different color channels. As a result, color artifacts appear in a denoised image obtained by using the existing methods. In this paper, we propose a novel non-local and inter-channel dependency-aware prior called the weighted tensor nuclear norm (WTNN). The proposed prior is derived by incorporating inter-channel dependency to low-rank-based NLSS prior. The WTNN is a low-rankness-of-the-third-order patch tensor, and we apply it to the tensors constructed with non-local similar patches. It enables us to naturally represent the higher-order dependencies among similar color patches. We propose an image denoising algorithm using the WTNN and image restoration algorithm by using a non-trivial generalization of this algorithm. The experimental results clearly show that the proposed WTNN-based color image denoising and restoration algorithms outperform state-of-the-art methods.