No-Reference Image Quality Assessment (NR-IQA) serves as a fundamental task in computer vision that aims to predict image quality consistent with human perception. Currently, numerous NR-IQA methods often use simplistic fusion strategies to integrate features from different backbones. However, these methods typically neglect the semantic differences and intricate inter-channel interactions among different features, thereby limiting their abilities to represent features effectively. To address this issue, we propose a Semantic-guided Channel Cross-attention Integration Network for NR-IQA (SCCIN-IQA), which enables more effective integration of complementary information from different backbones. The core module of our method is the fusion-semantic channel cross-attention. It first generates a semantic feature by integrating features from different backbones, then utilizes this semantic feature as a query to integrate the original backbone features via a channel cross-attention mechanism, thereby adaptively highlighting quality-relevant channel activations. Additionally, a space-channel enhancement module is introduced to further enhance the learned features in both space and channel dimensions, enabling comprehensive modeling of multi-dimensional contextual dependencies. Extensive experiments conducted on multiple public datasets demonstrate that the proposed SCCIN-IQA achieves state-of-the-art performance, consistently surpassing several mainstream methods while exhibiting strong generalization.

No-Reference Image Quality Assessment (NR-IQA) is an important task in the field of computer vision. Most methods utilize the pre-trained features with information irrelevant to image quality. In addition, some methods directly regress the pre-trained features without interaction or simply concatenate all features for score regression. They ignore the differences between local and global features. These issues lead to the limited IQA performance. To address this, we propose a Hybrid-Attention Refinement and Hierarchical Fusion Network (HAR-HFNet) for NR-IQA, which consists of a feature extraction module, a hybrid attention refinement module, a hierarchical dilated-attention fusion module and a quality prediction module. Firstly, the hybrid attention refinement module filters and refines the multi-stage features extracted by the pre-trained Swin Transformer, which enhances distortion-related information. Secondly, the hierarchical dilated-attention fusion module fuses the deep global feature with local features. It enables effective hierarchical integration of global semantics and local details. Finally, the quality prediction module predicts the score through weighted feature aggregation. Experiments on six public IQA datasets demonstrate that the HAR-HFNet outperforms some baseline NR-IQA methods in prediction accuracy and generalization ability.

No-Reference Image Quality Assessment (NR-IQA) is a fundamental computer vision task. In this paper, we propose a new Attention-Enhanced Feature Fusion Network for NR-IQA (AEFF-IQA) that integrates multi-scale local and non-local features. Firstly, multi-scale local and non-local features containing distortion information at semantic level are extracted by a feature extraction module consisting of two pre-trained deep neural networks. Secondly, a self-attention enhanced fusion module with four self-attention enhanced fusion components fuses the local and non-local features at the same scale to obtain multi-scale fusion features. Then, a cross-attention enhanced fusion module containing three cross-attention enhanced fusion components integrates these fusion features across multiple scales. Finally, the quality score is obtained by using a prediction module. Extensive experiments are conducted on five public datasets. The results show that our AEFF-IQA outperforms some state-of-the-art models and exhibits good generalization performance.

Most data-driven deep learning-based Artistic Image Aesthetics Assessment (AIAA) methods cannot effectively extract visual attributes from art images since the existing artistic image datasets don’t provide any information about visual attributes. The lack of visual attributes reduces the interpretability of AIAA methods and limits their performance. To address these problems, a novel artistic image aesthetics assessment assisted by photographic visual attributes is proposed. The proposed method consists of a feature extraction module and a joint prediction module. The feature extraction module pre-trained on a photographic dataset and an artistic image dataset can learn the information of photographic attributes and the generic artistic aesthetic information. The joint prediction module uses a non-local self-attention block to fuse the photographic visual attribute features with general artistic aesthetic features. The fused features are fed into an FC layer for calculating the artistic image aesthetic score. Experimental results indicate that our proposed method outperforms some state-of-the-art AIAA methods.

Fine-grained image retrieval (FGIR) is an important topic of image retrieval, and its challenge lies in the accurate identification of image objects with minor inter-class differences and considerable intraclass differences. Most existing methods exploit Convolutional Neural Networks (CNNs) to capture fine-grained and coarse-grained information while overlooking the scale variations. To address these issues, a novel method named Hash Generation Network (HGNet) guided by high frequency information is developed to learn crucial details across different scales. The HGNet consists of a High-Frequency Guidance Module (HFGM) and a Hash Generation Module (HGM). The key contribution is the proposed HFGM which integrates the high-frequency information and multi-scale features extracted from the Swin Transformer. As the Swin Transformer can effectively capture global contextual information, its multi-scale features, guided by high-frequency information that contains fine-grained texture details, can represent both fine-grained and coarse-grained details, thereby guiding the HGM in generating discriminative hash codes. Experimental results show that the HGNet outperforms several SOTA FGIR methods in retrieval performance.

No-Reference Image Quality Assessment (NR-IQA) is an important task of computer vision. Most deep neural networks based NR-IQA methods have the ability of accurate quality predictions, but they have large-scale parameters and high computational complexity. To alleviate these problems, we propose a lightweight NR-IQA method by unifying statistical and refined semantic features. Our proposed method consists of a lightweight feature extractor, a Statistical Semantic Feature Extraction (SSFE) module, and a Refined Semantic Feature Extraction (RSFE) module. The lightweight feature extractor is used to extract semantic features with perceptual distortion information. The SSFE module is designed to obtain statistical information of the semantic features for capturing the local and global changes of distorted image. The RSFE module is designed to refine the semantic features for measuring complex distortions. Extensive experiments on many IQA datasets are done and the results indicate that our proposed method outperforms some baseline NR-IQA methods in IQA performance, generalization ability, and model complexity.

Image aesthetics assessment is commonly treated as a classification or regression task and its performance bottleneck mainly depends on the effective utilization of aesthetic features. Traditional methods tend to use only subjective or objective features. Only one type of feature overlooks the inherent unity of these features in aesthetic characteristics. To comprehensively utilize image aesthetic characteristics, we propose an innovative image aesthetics assessment method that integrates subjective and objective features. Specifically, the proposed method comprises two modules: feature extraction module and feature fusion module. In the feature extraction module, a dual-branch neural network is proposed to extract subjective and objective aesthetic features of the image. In the feature fusion module, a feature fusion module based on a deep feedforward neural network is proposed to combine these two types of features and generate the final image aesthetic score. The experimental results indicate that our method gets a high performance in aesthetics assessment.

Dividing a Screen Content Image (SCI) with complex components into pictorial and textual regions for predicting scores is one of the common Screen Content Image Quality Assessment (SCIQA) methods. However, how to efficiently leverage pictorial and textual features to predict quality scores for no-reference SCIQA still needs to be explored. In addition, statistical analysis reveals that labels of SCIs present a distribution. Therefore, both the distribution of quality scores of SCIQA and the distribution of labels need to be considered in the SCIQA. This paper proposes a no-reference SCIQA method unifying pictorial and textual features. One contribution is the proposed dual-branch extraction module with the parameter-free attention convolution block and the joint prediction module. The proposed method employs the dual-branch extraction module to generate efficient pictorial and textual features and then uses the joint prediction module to predict quality scores. Another contribution is the joint distribution loss. It makes the distribution of the quality scores as close as possible to the distribution of labels. Experiments on the SCIQA datasets show that the proposed method achieves excellent SCIQA performance and generalization ability.

Image quality assessment (IQA) is an important task of computer vision. Most full-reference (FR) IQA methods do not reach desirable prediction performance. To address this issue, we propose a novel multi-level feature aggregation network (MLFAN) for FR-IQA. An important contribution is an effective multi-level feature aggregation network. This network utilizes a siamese network with vision transformer for multi-level feature extraction. It compares images at the multi-level perceptual feature differences by considering the relationship among color, texture and shape information, focuses more on the salient regions by an attention aggregator and scores images by a two-branch prediction head. Another important contribution is a novel loss function. This loss function jointly utilizes Mean Square Error, KL divergence and rank order of quality scores to provide stable training. It makes the proposed MLFAN-IQA method effectively learn perceptual quality of images. Experiments are done to test IQA performance of the proposed MLFAN-IQA method. Comparisons show that the proposed MLFAN-IQA method outperforms some state-of-the-art FR-IQA methods on the datatsets of conventional distorted images. Moreover, the proposed MLFAN-IQA method also reaches comparable performance on the dataset of GAN-based synthetic distorted images.