Mobile games have played an increasingly significant role in people's leisure lives in recent years, thanks to the fast expansion of the gaming industry and the widespread use of mobile devices. The aesthetic quality of game pictures is a very important factor that attracts users' interest. However, evaluating the aesthetic quality of mobile game pictures is difficult since the painting styles of games vary greatly and the evaluation criteria are also diversified. In this article, we propose a multitask deep learning-based method, which is able to predict the aesthetic quality of mobile game images in multiple dimensions. The proposed model consists of two modules, a feature extraction module and a quality regression module. We extract quality-aware features from intermediate layers of the deep convolution neural network and then incorporate them into the final feature representation in the feature extraction module, allowing the model to fully use visual information from low to high levels. The quality regression module uses fully connected layers to map quality-aware features into quality scores across multiple dimensions. The multidimensional aesthetic quality scores are trained using a multitask learning approach, in which quality-aware features are shared across multiple dimensional quality prediction tasks. Finally, several key factors which help the proposed model perform better are analyzed. The experimental results indicate that our proposed method not only achieves the greatest performance on mobile game images, but also is applicable to natural scene images.

Surveillance videos have been widely used in many vision-based systems, supporting intelligent tasks such as object detection and tracking. However, the quality of surveillance videos suffers from poor weather conditions and inevitable compression error, which may have a negative influence on the performance of such tasks. Therefore, accurately distinguishing distortions and predicting severity levels are crucial. In this paper, we propose a quality related retraining framework as well as a no-reference (NR) multi-task video quality assessment (VQA) model to tackle the challenge of surveillance videos quality assessment. The quality related retraining framework operates on a synthetic VQA database. The proposed NR VQA method utilizes both spatial and temporal information by using ResNet50 and SlowFast. Then multiple distortion detection heads are applied to predict the severity levels for corresponding distortions. The experimental results show that the proposed method gains competitive performance on the Video Surveillance Quality Assessment Dataset (VSQuAD). The ablation study further confirms the contributions of the quality related retraining framework, spatial information, and temporal information.

To support the application scenarios where high-resolution (HR) images are urgently needed, various single image super-resolution (SISR) algorithms are developed. However, SISR is an ill-posed inverse problem, which may bring artifacts like texture shift, blur, etc. to the reconstructed images, thus it is necessary to evaluate the quality of super-resolution images (SRIs). Note that most existing image quality assessment (IQA) methods were developed for synthetically distorted images, which may not work for SRIs since their distortions are more diverse and complicated. Therefore, in this paper, we propose a no-reference deep-learning image quality assessment method based on frequency maps because the artifacts caused by SISR algorithms are quite sensitive to frequency information. Specifically, we first obtain the high-frequency map (HM) and low-frequency map (LM) of SRI by using Sobel operator and piecewise smooth image approximation. Then, a two-stream network is employed to extract the quality-aware features of both frequency maps. Finally, the features are regressed into a single quality value using fully connected layers. The experimental results show that our method outperforms all compared IQA models on the selected three super-resolution quality assessment (SRQA) databases.

With the development of rendering techniques, computer graphics generated images (CGIs) have been widely used in practical application scenarios such as architecture design, video games, simulators, movies, etc. Different from natural scene images (NSIs), the distortions of CGIs are usually caused by poor rending settings and limited computation resources. What's more, some CGIs may also suffer from compression distortions in transmission systems like cloud gaming and stream media. However, limited work has been put forward to tackle the problem of computer graphics generated images' quality assessment (CG-IQA). Therefore, in this paper, we establish a large-scale subjective CG-IQA database to deal with the challenge of CG-IQA tasks. We collect 25,454 in-the-wild CGIs through previous databases and personal collection. After data cleaning, we carefully select 1,200 CGIs to conduct the subjective experiment. Several popular no-reference image quality assessment (NR-IQA) methods are tested on our database. The experimental results show that the handcrafted-based methods achieve low correlation with subjective judgment and deep learning-based methods obtain relatively better performance. The current NR-IQA models are not suitable for CG-IQA tasks and more effective models are urgently needed.

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.

To improve the viewer’s Quality of Experience (QoE) and optimize computer graphics applications, 3D model quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most widely used digital representation formats of 3D models, the visual quality of which is quite sensitive to lossy operations like simplification and compression. Therefore, many related studies such as point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality of distorted 3D models. However, most previous studies utilize full-reference (FR) metrics, which indicates they can not predict the quality level in the absence of the reference 3D model. Furthermore, few 3D-QA metrics consider color information, which significantly restricts their effectiveness and scope of application. In this paper, we propose a no-reference (NR) quality assessment metric for colored 3D models represented by both point cloud and mesh. First, we project the 3D models from 3D space into quality-related geometry and color feature domains. Then, the 3D natural scene statistics (3D-NSS) and entropy are utilized to extract quality-aware features. Finally, a support vector regression (SVR) model is employed to regress the quality-aware features into visual quality scores. Our method is validated on the colored point cloud quality assessment database (SJTU-PCQA), the Waterloo point cloud assessment database (WPC), and the colored mesh quality assessment database (CMDM). The experimental results show that the proposed method outperforms most compared NR 3D-QA metrics with competitive computational resources and greatly reduces the performance gap with the state-of-the-art FR 3D-QA metrics. The code of the proposed model is publicly available now athttps://github.com/zzc-1998/NR-3DQA.

Low-light images, which are usually taken in dark or back-lighting conditions, are hard to perceive due to the low visibility and low contrast. To improve viewers’ Quality of Experience (QoE) and support the application of vision-based systems, various low-light image enhancement algorithms (LIEAs) have been proposed to lighten low-light images. However, some LIEAs may amplify the hidden distortions in the dark like noise and even further, introduce new distortions such as structural damage, color shift, etc, which severely affect the quality of light-enhanced images and need to be evaluated quantificationally. However, in the literature, few measures are proposed to assess the quality of light-enhanced images. Therefore, in this paper, we develop a no-reference low-light image enhancement evaluation (NLIEE) metric to predict the quality of light-enhanced images. The image quality is mainly assessed from four key aspects: light enhancement, color comparison, noise measurement, and structure evaluation. The experiment results show that NLIEE achieves the best performance among the general no-reference image quality assessment (NR IQA) models and quality descriptors for light enhancement.

With the development of the game industry and the popularization of mobile devices, mobile games have played an important role in people's entertainment life. The aesthetic quality of mobile game images determines the users' Quality of Experience (QoE) to a certain extent. In this paper, we propose a multi-task deep learning based method to evaluate the aesthetic quality of mobile game images in multiple dimensions (i.e. the fineness, color harmony, colorfulness, and overall quality). Specifically, we first extract the quality-aware feature representation through integrating the features from all intermediate layers of the convolution neural network (CNN) and then map these quality-aware features into the quality score space in each dimension via the quality regressor module, which consists of three fully connected (FC) layers. The proposed model is trained through a multi-task learning manner, where the quality-aware features are shared by different quality dimension prediction tasks, and the multi-dimensional quality scores of each image are regressed by multiple quality regression modules respectively. We further introduce an uncertainty principle to balance the loss of each task in the training stage. The experimental results show that our proposed model achieves the best performance on the Multi-dimensional Aesthetic assessment for Mobile Game image database (MAMG) among state-of-the-art image quality assessment (IQA) algorithms and aesthetic quality assessment (AQA) algorithms.

Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.

Smartphone is the superstar product in digital device market and the quality of smartphone camera photos (SCPs) is becoming one of the dominant considerations when consumers purchase smartphones. How to evaluate the quality of smartphone cameras and the taken photos is urgent issue to be solved. To bridge the gap between academic research accomplishment and industrial needs, in this paper, we establish a new Smartphone Camera Photo Quality Database (SCPQD2020) including 1800 images with 120 scenes taken by 15 smartphones. Exposure, color, noise and texture which are four dominant factors influencing the quality of SCP are evaluated in the subjective study, respectively. Ten popular no-reference (NR) image quality assessment (IQA) algorithms are tested and analyzed on our database. Experimental results demonstrate that the current objective models are not suitable for SCPs, and quality metrics having high correlation with human visual perception are highly needed.