# Understanding How Image Quality Affects Transformer Neural Networks  
[PERSON]  
1 Nokia Bell Labs, 1083 Budapest, Hungary; domonkos.varga@nokia-bell-labs.  
to obstacles [11]. Visual quality evaluation helps in filtering out poor-quality images that might introduce noise or errors ensuring that the algorithms work on clear and accurate images [12]. In multimedia applications, such as streaming video services or gaming, visual quality is a key factor [13]. Assessing and maintaining high-quality visuals ensures smooth content delivery, reduces buffering, and enhances the overall viewing experience for users [14]. In augmented reality (AR) and virtual reality (VR), visual quality also is paramount for immersion and user comfort. Poor-quality visuals can break immersion and even cause motion sickness [15]. For example, AR applications on mobile devices must constantly evaluate the visual quality of the environment they are augmenting to ensure smooth and accurate overlay of virtual objects [16]. This requires real-time visual quality evaluation to adjust rendering and processing based on environmental conditions. In machine learning, visual quality evaluation is essential for curating high-quality datasets. Annotated data used for training computer vision models must be of high visual quality to ensure accurate model performance. This is particularly important in applications like object recognition [17] and image segmentation [18]. In security applications, such as facial recognition systems, visual quality evaluation is essential for identifying individuals accurately [19]. In scenarios with low light or poor camera angles, the quality of the captured images may suffer, leading to false positives or negatives. Evaluating and ensuring high visual quality can mitigate these issues, leading to more reliable security systems. In manufacturing, computer vision systems are used to detect defects in products [20]. Visual quality evaluation ensures that images captured by inspection cameras are clear enough to detect even the smallest flaws. For example, in electronics manufacturing, even minor defects in circuit boards need to be detected, which requires high-quality image capture and analysis [21]. As can be seen from the above examples, visual quality evaluation is a cornerstone of effective computer vision applications. It ensures that the data used by models are of sufficient quality to produce reliable results and that the output meets the standards required for the specific use case.  
In computer vision, neural network-based solutions and algorithms have achieved state-of-the-art results in a huge number of domains, i.e., image classification [22], image segmentation [23], action recognition [24], etc. In spite of their excellent performance in computer vision tasks, it was demonstrated that deep neural networks are sensitive to adversarial samples [25]. These adversarial samples are perturbations that are often imperceptible to humans, but they can significantly affect the model's output. They can be generated using various techniques, including optimization algorithms that iteratively adjust the input to maximize the model's prediction error [26, 27]. In [28], [PERSON] et al. improved an adversarial attack against a stream of online images by implementing an optimization method to eliminate weight decay loss from the total loss term of models. Further, [PERSON] and [PERSON] [29] pointed out that convolutional neural networks (CNNs) are very susceptible to certain distortion types and surprisingly resilient to JPEG or JPEG2000 compression noise.