In recent years, AI-Generated Images (AIGIs) have attracted significant attention and shown great potential in various applications, including entertainment, advertisement, education, and product design. Driven by this trend, various Text-to-Image (T2I) models are developed. However, the quality of AIGIs produced by these models varies widely, with many low-quality images failing to meet human aesthetic standards. Consequently, research into both subjective and objective Image Quality Assessment (IQA) methods for AIGIs is crucial. In this paper, we introduce a dataset called AIGI-IQAD, designed to enhance our understanding of human aesthetic preferences for AIGIs. The dataset contains 2,880 AIGIs generated by 8 T2I models using 360 deliberately designed text prompts. Further, we conducted subjective experiments to gather ratings from both aesthetic quality and text-image consistency. Building on this dataset, we propose a model named Question-guided Multimodal Interaction Network (QMI-Net) for evaluating AIGIs. QMI-Net assesses human preferences for AIGIs by focusing on both aesthetic quality and text-image consistency. Specifically, QMI-Net uses a question-answering approach to guide Multimodal Large Language Models (MLLMs) in generating detailed aesthetic and similarity information. The Visual and Aesthetic Feature Fusion Module (VAFFM) then fuses the aesthetic features with the visual features extracted by Contrastive Language-Image Pre-training (CLIP) to obtain more comprehensive aesthetic quality features. Comprehensive experiments demonstrate that state-of-the-art performance is achieved by QMI-Net on our AIGI-IQAD and three other public datasets.The AIGI-IQAD datasets and QMI-Net will be released athttps://github.com/ctxya1207/QMI-Net.

As an important technology in the fields of intelligent transportation and public safety, crowd counting that can obtain pedestrian flow information has attracted extensive attention from academic and industrial communities. However, existing RGB-T crowd counting methods cannot effectively balance the counting accuracy and computational complexity in practical applications. For this, we propose a Mutual Head Knowledge Distillation Framework (MHKDF) to obtain a lightweight RGB-T crowd counting network for efficient and accurate pedestrian number estimation. Specifically, to avoid the influence of parameter and structure differences between teacher and student networks on the distillation effect, we propose a Cooperative Mutual Knowledge Distillation (CMKD) strategy to comprehensively and dynamically transfer the crowd analysis ability of the complex teacher model (MHKDF-T) to the lightweight student model (MHKDF-S). In addition, the upper bound of the performance of the student network depends on the teacher model with high accuracy. Therefore, to take advantage of the complementary advantages of frequency domain and spatial domain feature fusion, we propose a Multi-Modal Spatial-Frequency Hybrid Fusion Module (MSFHFM) to futher improve counting accuracy of MHKDF-T. Comprehensive experiments on two RGB-T crowd counting datasets demonstrate that our MHKDF-S achieves competitive performance with only 5.68 FLOPs and 4.89M parameters. Our code will be released at https://github.com/BaoYangCC/MHKDF.

Due to the emergency of multi-view cameras and commercial Light Field (LF) cameras, the demand of high-performance LF quality evaluator is of great significance for guiding LF acquisition, processing and application and further promoting the visual perceived quality of LF visualizations. However, LF Images (LFIs), as high-dimensional data, suffer from various quality degradations not only in the spatial domain but also in the angular domain. Therefore, it is of great challenge to predict LF quality accurately. An effective LF evaluator should be able to represent these heterogeneous artifacts. In this paper, we provide a novel No-Reference LF Quality Assessment Evaluator (NR LF-QAE) to tackle this problem. Firstly, to measure angular consistency among viewports, we utilize group-based representations to character information similarity of aligned view stacks. Secondly, to better describe the texture information of LFIs, unifying spatial-angular texture statistic measurement is performed via Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). Thirdly, we design 3D Log-Gabor filters to extract LF global structure information in Sub-Aperture Images (SAIs) as spatial feature characterizations and 2D Log-Gabor filters are adopted to characterize ray direction/depth information in Epipolar Plane Images (EPIs) as angular feature characterizations. By comprehensive LF information analyses in angular consistency and spatial-angular feature extraction with texture and structure descriptors, experimental results demonstrate the superiority of the proposed NR LF-QAE over the state-of-the-art comparative models in predicting the quality of LFIs on three available benchmark databases. The code will be released athttps://github.com/zerosola/NR-LF-QAE.

Night-time image enhancement (NIE) aims at boosting the intensity of low-light regions while suppressing noises or light effects in night-time images, and numerous efforts have been made for this task. However, few explorations focus on the quality evaluation issue of enhanced night-time images (ENTIs), and how to fairly compare the performance of different NIE algorithms remains a challenging problem. In this paper, we firstly construct a new Real-world Night-Time Image Enhancement Quality Assessment (i.e., RNTIEQA) dataset that includes two typical types of night-time scenes (i.e., extremely low light and uneven light scenes), and carry out human subjective studies to compare the quality of ENTIs obtained by a set of representative NIE algorithms. Afterwards, a new objective ranking method that comprehensively considering image intrinsic and impairment attributes is proposed for automatically predicting the quality of ENTIs. Experimental results on our RNTIEQA dataset demonstrate that the proposed method outperforms the off-the-shelf competitors. Our dataset and code will be released athttps://github.com/Leilei-Huang-work/RNTIEQA-dataset.

In the animation industry, automatically predicting the quality of cartoon images based on the inputs of general distortions and color change is an urgent task, while the existing no-reference (NR) methods usually measure the perceptual quality of the natural images. In this paper, based on the observation that structure and color are the main factors affecting cartoon images quality, we proposed a new NR quality prediction metric for cartoon images, which fully takes gradient and color information into account. The experimental results on our newly constructed NBU-CIQAD dataset with color change and other existing cartoon image dataset demonstrate that the proposed method significantly outperforms existing no-references methods for the task of cartoon image quality assessment. The database and code will be released athttps://github.com/1010075746/NBU-CIQAD.

Stereoscopic imageretargeting (SIR) techniques attempt to display stereoscopic images on stereoscopic devices of various resolutions and aspect ratios to provide the users with better viewing experience. However, new quality perceptual problems emerge in the retargeted stereoscopic images generated by current SIR operators are quite different from those in the retargeted 2D images. In this paper, we dedicate to exploring the perceptual quality-related factors (e.g., shape preservation, object preservation and visual comfort.) of retargeted stereoscopic images, and propose a novel quality evaluation metric for SIR to achieve a more consistent evaluation with 3D perception and image degradation mechanism in the SIR process. Moreover, image quality features and 3D perceptual features are integrated into one representation for an overall perceptual quality prediction using a list-wise ranking approach, which gives priority to the ranking among the SIR results generated from the same stereoscopic source. Experimental results demonstrate that the proposed method outperforms most quality models developed for retargeted 2D/stereoscopic images.

Stereoscopic Image Retargeting (SIR) aims to adapt stereoscopic images and videos to 3D display devices with various aspect ratios by emphasizing the important content while retaining surrounding context with minimal visual distortion. To address the issue of SIR evaluation, this paper presents a new objective quality assessment method for retargeted stereopairs by combining image quality and depth perception measures. Specifically, the image quality measure is conducted between the source and retargeted intermediate views generated by the view synthesis method to characterize the geometric distortion and content loss of the retargeted stereopair, while several depth-aware features are extracted to measure the visual comfort/discomfort and depth sensation when human views a 3D scene. Then, the extracted features are integrated into an overall perceptual quality prediction. Experiment results on NBU SIRQA and SIRD databases verify the superiority of our method.

Tone mapping operators (TMOs) are developed to convert a high dynamic range (HDR) image into a low dynamic range (LDR) one for display with the goal of preserving as much visual information as possible. However, image quality degradation is inevitable due to the dynamic range compression during the tone-mapping process. This accordingly raises an urgent demand for effective quality evaluation methods to select a high-quality tone-mapped image (TMI) from a set of candidates generated by distinct TMOs or the same TMO with different parameter settings. A key element to the success of TMI quality evaluation is to extract effective features that are highly consistent with human perception. Towards this end, this paper proposes a novel blind TMI quality metric by exploiting both local degradation characteristics and global statistical properties for feature extraction. Several image attributes including texture, structure, colorfulness and naturalness are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art blind quality models designed for synthetically distorted images (SDIs) and the blind quality models specifically developed for TMIs.

A synthesized view can be generated via Depth-Image-Based Rendering (DIBR) technique using one (or more) color images and the associated depth maps. However, several artifacts may occur in the synthesized views due to the imperfect color images, depth maps or texture inpainting techniques, which cannot be effectively estimated by the conventional quality metrics designed for natural images. In this paper, a new quality metric is proposed to evaluate DIBR-synthesized images by measuring texture and geometric distortions. The artifacts are first analyzed on different phases of the synthesis process, and the associated features are extracted to estimate the degree of texture and geometric distortions from both coarse and fine scales. Finally, individual quality scores are aggregated into an overall quality via regression. Experimental results on three publicly available DIBR datasets demonstrate the superiority of the proposed method over the state-of-the-art quality models.