ABSTRACT Panoramic image stitching algorithms inevitably introduce distortions due to their inherent limitations. Due to the characteristics of the Human Visual System (HVS), slight stitching distortions have a minimal impact on perceived image quality, whereas severe distortions significantly degrade it. To address this issue, a visual perception‐based quality assessment method for stitched panoramic images is proposed. Existing no‐reference image quality assessment (NR‐IQA) methods often struggle to accurately model the HVS and its subjective perception of stitching distortions, leading to significant discrepancies between predicted and actual quality scores. Therefore, we use the learnt perceptual image patch similarity (LPIPS) to capture perceptual similarity from the HVS, thereby generating a high‐quality pseudo‐reference image (PRI). To further improve its perceptual quality, the PRI is refined using a visual restoration network (VRN) to reduce residual distortions. Subsequently, both the pseudo‐reference and distorted images are fed into a quality assessment network with shared parameters to extract deep feature representations. To improve distortion‐aware quality evaluation, we design an Adaptive Feature Fusion (AFF) module, where features from the pseudo‐reference and distorted images are first enhanced through an attention mechanism and then adaptively fused via learnt weights for more effective quality assessment. Finally, the fused features are processed through fully connected layers for regression, predicting the perceptual quality score of the stitched panoramic image. Extensive experimental results demonstrate that the proposed method outperforms state‐of‐the‐art approaches, achieving significant improvements across multiple evaluation metrics.

Abstract The quality of images generated by computer graphics rendering algorithms is mainly affected by visible distortion at some pixel locations. Image quality assessment (IQA) metrics are commonly utilized to assess the quality of rendered images, but their results are a global difference value, which does not provide pixel‐wise differences to optimize the renderings. In contrast, visibility difference models including visual perception models and deep learning models can calculate pixel‐wise visibility difference between distorted images and reference images. However, they either are only applied to a single type of visible distortion or are seriously dependent on datasets. To this end, the authors propose a novel model, dubbed Human Visual Perception and Deep Learning Image Difference Metric (HPDL‐IDM), which combines the Human Visual System (HVS) model and deep learning. HPDL‐IDM primarily consists of two modules: (i) the visual perception feature calculation module, which calculates difference maps between various kinds of features extracted from the reference image and the distorted image according to the visual characteristics of human eyes and concatenates them, and (ii) the deep learning module, which utilizes a neural network of encoder–decoder structure to train on the LocvisVC and VisTexRes datasets whose input and output are these concatenated feature difference maps and the final image distortion visibility difference map respectively. Additionally, the authors pool the final difference map into a global difference value between 0 and 1 to apply their model to many image processing tasks related to Image quality metrics (IQMs). Experimental results show that HPDL‐IDM's generalization capacity and accuracy are improved by a large margin compared to other models.

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security.