Blind Image Quality Assessment, aiming to replicate human perception of visual quality without reference, plays a key role in vision tasks, yet existing models often fail to effectively capture subtle distortion cues, leading to a misalignment with human subjective judgments. We identify that the root cause of this limitation lies in the lack of reliable distortion priors, as methods typically learn shallow relationships between unified image features and quality scores, resulting in their insensitive nature to distortions and thus limiting their performance. To address this, we introduce DR.Experts, a novel prior-driven BIQA framework designed to explicitly incorporate distortion priors, enabling a reliable quality assessment. DR.Experts begins by leveraging a degradation-aware vision-language model to obtain distortion-specific priors, which are further refined and enhanced by the proposed Distortion-Saliency Differential Module through distinguishing them from semantic attentions, thereby ensuring the genuine representations of distortions. The refined priors, along with semantics and bridging representation, are then fused by a proposed mixture-of-experts style module named the Dynamic Distortion Weighting Module. This mechanism weights each distortion-specific feature as per its perceptual impact, ensuring that the final quality prediction aligns with human perception. Extensive experiments conducted on five challenging BIQA benchmarks demonstrate the superiority of DR.Experts over current methods and showcase its excellence in terms of generalization and data efficiency.

Efficient visual backbone design remains crucial for resource-constrained computer vision applications. Inspired by the adaptive continuous-time dynamics observed in biological neurons, we propose FVNet, a novel lightweight architecture that integrates liquid neural dynamics for efficient and dynamic visual feature extraction. Central to FVNet is the Fluid Temporal Flow Unit (FTFU), which employs continuous-time equations with learnable time constants to capture spatio-temporal dependencies adaptively. By further stacking these units in a Multi-Phase Fluid Block (MPFB), our model processes features across parallel temporal scales, enabling context-aware feature encoding without incurring excessive computational overhead. Through a discrete closed-form solution, FVNet achieves the representational power of continuous-time models while avoiding the instability and overhead of iterative numerical solvers. Extensive experiments on various vision tasks demonstrate that FVNet achieves superior performance and efficiency over existing state-of-the-art lightweight networks.

Vision-Language Models (VLMs) have demonstrated significant progress in quality assessment tasks. However, a fundamental paradox arises when their application to Point Cloud Quality Assessment (PCQA). Existing VLMs, designed for image-text pairs, are inherently incompatible with 3D point cloud data due to the modality gap. While some PCQA research attempts to adapt point clouds to VLMs by 2D projection, this approach inevitably sacrifices crucial spatial structure information essential for accurate quality assessment. Conversely, directly integrating a dedicated 3D branch into a VLM-based PCQA framework introduces feature space misalignment and an influx of quality-insensitive information. To bridge these fundamental conflicts hindering VLMs' adaptation to PCQA, we propose the PMP-PCQA framework, which leverages the inherent mapping relationship between points and pixels to seamlessly apply VLMs to PCQA. Our approach introduces three key innovations: a Spatial Awareness Enhancer(SAE) module that enriches the image features with spatial coordinate clues to reinforce geometric awareness in 2D visual representations; a Fine-to-coarse Consistency Alignment(FCA) module that bridges the gap between 2D and 3D modalities by leveraging point-pixel correspondences to construct bridging features; and a Text-Guided Adaptive Miner(TAM) module that dynamically suppresses quality-insensitive features to mine discriminative visual clues for PCQA. Extensive evaluations demonstrate that PMP-PCQA consistently outperforms state-of-the-art methods across multiple benchmarks.

Point Cloud Quality Assessment (PCQA) faces a critical disconnect: existing methods operate on a flawed single-perception paradigm, while human observers evaluate quality through dual cognitive streams: technical rationality and semantic sensibility. This fundamental mismatch routinely produces assessment failures in real-world scenarios where technical and semantic signals conflict. To address this, we introduce Dual-Stream Perception PCQA (DSP-PCQA), the first framework that explicitly models this perceptual duality through parallel networks thoroughly mirroring the human cognitive pathway. DSP-PCQA introduces three key innovations: (1) a Decoupled Focus Enhancer (DFE) that surgically isolates technical and semantic information using two targeted transformations; (2) a Context & Attribute Correlation Awareness (CACA) module that captures the dynamic, non-linear relationships between different views and sub-models characteristic of human visual processing; and (3) an Exchange-based Perceptual Injection (EPI) module that strategically transfers information between perception streams, simulating how humans integrate multiple perceptual dimensions. Extensive evaluations show DSP-PCQA outperforms state-of-the-art methods across multiple benchmarks. Most importantly, our method resolves the perceptual discord that plagues existing approaches, maintaining high accuracy even in the challenging boundary cases where technical quality and semantic significance diverge, precisely where conventional methods often struggle.

Super-resolution (SR) techniques are critical for enhancing image quality, particularly in scenarios where high-resolution imagery is essential yet limited by hardware constraints. Existing diffusion models for SR have relied predominantly on Gaussian models for noise generation, which often fall short when dealing with the complex and variable texture inherent in natural scenes. To address these deficiencies, we introduce the Bayesian Uncertainty Guided Diffusion Probabilistic Model (BUFF). BUFF distinguishes itself by incorporating a Bayesian network to generate high-resolution uncertainty masks. These masks guide the diffusion process, allowing for the adjustment of noise intensity in a manner that is both context-aware and adaptive. This novel approach not only enhances the fidelity of super-resolved images to their original high-resolution counterparts but also significantly mitigates artifacts and blurring in areas characterized by complex textures and fine details. The model demonstrates exceptional robustness against complex noise patterns and showcases superior adaptability in handling textures and edges within images. Empirical evidence, supported by visual results, illustrates the model's robustness, especially in challenging scenarios, and its effectiveness in addressing common SR issues such as blurring. Experimental evaluations conducted on the DIV2K dataset reveal that BUFF achieves a notable improvement, with a +0.61 increase compared to baseline in SSIM on BSD100, surpassing traditional diffusion approaches by an average additional +0.20dB PSNR gain. These findings underscore the potential of Bayesian methods in enhancing diffusion processes for SR, paving the way for future advancements in the field.

Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step toward exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, 1) we propose a Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual textual prompt conditions for the diffusion model. 2) we propose a Perceptual Conditional Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on textual prompt conditions. By incorporating a lightweight denoiser and requiring only a few feature denoising steps (e.g., just five iterations), our PFD-IQA framework achieves superior performance across eight standard BIQA datasets, validating its effectiveness.

In the Blind Image Quality Assessment (BIQA) field, accurately assessing the quality of authentically distorted images presents a substantial challenge due to the diverse distortion types in natural settings. Existing state-of-the-art IQA methods mix a sequence of distortions into entire images to establish global distortion priors, but are inadequate for authentic images with spatially varied distortions. To address this, we introduce a novel IQA framework that employs knowledge distillation tailored to perceive spatially heterogeneous distortions, enhancing quality-distortion awareness. Specifically, we introduce a novel Block-wise Degradation Modelling approach that applies distinct distortions to different spatial blocks of an image, thereby expanding local distortion priors. Following this, we present a Block-wise Aggregation and Filtering module that enables fine-grained attention to the quality information within different distortion areas of the image. Furthermore, to effectively capture the complex relationships between distortions across different regions while preserving overall quality perception, we introduce Contrastive Knowledge Distillation to enhance the model’s ability to discriminate between different types of distortions and Affinity Knowledge Distillation to model the correlation among distortions in different regions. Extensive experiments on standard BIQA datasets demonstrate the effectiveness and competitiveness of the proposed method.

Given an object mask, Semi-supervised Video Object Segmentation (SVOS) technique aims to track and segment the object across video frames, serving as a fundamental task in computer vision. Although recent memory-based methods demonstrate potential, they often struggle with scenes involving occlusion, particularly in handling object interactions and high feature similarity. To address these issues and meet the real-time processing requirements of downstream applications, in this paper, we propose a novel bOundary Amendment video object Segmentation method with Inherent Structure refinement, hereby named OASIS. Specifically, a lightweight structure refinement module is proposed to enhance segmentation accuracy. With the fusion of rough edge priors captured by the Canny filter and stored object features, the module can generate an object-level structure map and refine the representations by highlighting boundary features. Evidential learning for uncertainty estimation is introduced to further address challenges in occluded regions. The proposed method, OASIS, maintains an efficient design, yet extensive experiments on challenging benchmarks demonstrate its superior performance and competitive inference speed compared to other state-of-the-art methods, i.e., achieving the F values of 91.6 (vs. 89.7 on DAVIS-17 validation set) and G values of 86.6 (vs. 86.2 on YouTubeVOS 2019 validation set) while maintaining a competitive speed of 48 FPS on DAVIS.

Image assessment aims to evaluate the quality and aesthetics of images and has been applied across various scenarios, such as natural and AIGC scenes. Existing methods mostly address these sub-tasks or scenes individually. While some works attempt to develop unified image assessment models, they have struggled to achieve satisfactory performance or cover a broad spectrum of assessment scenarios. In this paper, we present Gamma, a Generic imAge assessMent model using Mixture of Assessment Experts, which can effectively assess images from diverse scenes through mixed-dataset training. Achieving unified training in image assessment presents significant challenges due to annotation biases across different datasets. To address this issue, we first propose a Mixture of Assessment Experts (MoAE) module, which employs shared and adaptive experts to dynamically learn common and specific knowledge for different datasets, respectively. In addition, we introduce a Scene-based Differential Prompt (SDP) strategy, which uses scene-specific prompts to provide prior knowledge and guidance during the learning process, further boosting adaptation for various scenes. Our Gamma model is trained and evaluated on 12 datasets spanning 6 image assessment scenarios. Extensive experiments show that our unified Gamma outperforms other state-of-the-art mixed-training methods by significant margins while covering more scenes. Codes are available at https://github.com/zht8506/Gamma.

Underwater acoustic target recognition (UATR) can be significantly empowered by advancements in deep learning (DL). However, the effectiveness of DL-based UATR methods is often constrained by the limited computing resources available on underwater platforms. Most of the existing knowledge distillation (KD) strategies try to build lightweight DL models, but these strategies rarely consider the acoustic properties of underwater environments, making them less efficient for UATR tasks. Thus, fully harnessing the potential of DL techniques while ensuring the model’s practicality, is one of the urgent problems to be solved in UATR research. In this work, we introduce the union-domain KD (UDKD) to establish an accurate and lightweight UATR model. UDKD integrates two KD strategies: dual-frequency band distillation (DBD) and cross-domain masked distillation (CMD). DBD improves the learning process for a simple student model by decoupling the knowledge of spectrograms into the local structural (i.e., line spectra) and global composition (i.e., propagation patterns) aspects. CMD reduces redundant information from the Fourier Transform process, enabling the student model to concentrate on essential signal elements and to learn underlying time–frequency distribution. Extensive experiments on two real-world oceanic datasets confirm the superior performance of UDKD compared to existing KD methods, i.e., achieving an accuracy of 94.81% ($\uparrow ~3.19$% versus 91.62%). Notably, UDKD showcases a 10.5% improvement in the prediction accuracy of the lightweight student model.

Underwater imaging is essential in a variety of fields, including resource exploration, marine observation, and scientific research. However, the quality of underwater images is often compromised by environmental factors such as light scattering, absorption, and the presence of fog, leading to distortions such as color shifts, low contrast, and blurriness. To address these challenges, we propose a novel underwater image quality assessment (UIQA) method, the Attention and Mamba-driven Quality Index (AMQI). The AMQI model employs a multi-stage architecture designed to capture both local and global image features critical for underwater quality evaluation. First, a Shallow Feature Extractor (SFE) captures essential spatial details. Next, the Local Information Representation Network (LIR-Net), equipped with Channel Attention (CA) and Large Kernel-guided Spatial (LKS) mechanisms, enhances fine details and captures long-range dependencies to address underwater-specific distortions. The Global Information Representation Network (GIR-Net) further processes the features using a combination of the Visual State-Space Model (VSSM) and ResNet-50 to capture high-level semantic and contextual information. Finally, the Feature-Quality Mapping Network (FQM) converts the learned features into a quality score, ensuring precise predictions of image quality. Extensive experiments on the Underwater Image Quality Database (UIQD) demonstrate that AMQI outperforms current state-of-the-art IQA and UIQA models in terms of accuracy and correlation with human subjective evaluations. The model's robustness and generalization capabilities are further validated through detailed ablation studies and cross-database evaluations, showcasing its strong performance across diverse underwater environments. The source code is available athttps://github.com/ibaochao/AMQI.

The goal of underwater image enhancement (UIE) is to boost the acquired underwater image quality, which increases the value of the underwater image significantly. However, without effective underwater enhanced image quality assessment (UEIQA) measures that benchmark the UIE, the process of UIE becomes driftless and the enhanced results of different UIE algorithms cannot be fairly compared. Toward this end, we in this work construct a dedicated UEIQA scheme on the basis of deep investigation of the underwater enhanced image characteristics. Specifically, in our proposed method, we respectively design deep neural networks to represent the unique attributes of the underwater enhanced image, such as color cast, local distortions, naturalness degree, sharpness, contrast, fog density, etc., that are highly correlated with the image quality. Then we introduce the Vision Transformer (ViT) to capture the dependencies among different image attributes and infer the image quality level. Extensive experiments conducted on three typical UEIQA databases, i.e., SOTA, UID2021 and SAUD, show that the proposed UEIQA model yields noteworthy higher prediction accuracy than the representative IQA and UEIQA metrics, e.g., achieving SRCC values of 0.891 ( vs. 0.749 in SAUD) and 0.933 ( vs. 0.798 in UID2021). The proposed UEIQA model will be released athttps://github.com/YT2015?tab=repositories.

The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions. Regrettably, the commonly used parallel head often lacks omni perceptual capabilities, such as deformation perception (DP), global perception (GP), and cross-task perception (CTP). Despite numerous methods attempting to enhance these abilities from a single aspect, achieving a comprehensive and unified solution remains a significant challenge. In response to this challenge, we develop an innovative detection head, termed UniHead, to unify three perceptual abilities simultaneously. More precisely, our approach: 1) introduces DP, enabling the model to adaptively sample object features; 2) proposes a dual-axial aggregation transformer (DAT) to adeptly model long-range dependencies, thereby achieving GP; and 3) devises a cross-task interaction transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks. As a plug-and-play method, the proposed UniHead can be conveniently integrated with existing detectors. Extensive experiments on the COCO dataset demonstrate that our UniHead can bring significant improvements to many detectors. For instance, the UniHead can obtain +2.7 AP gains in RetinaNet, +2.9 AP gains in FreeAnchor, and +2.1 AP gains in GFL. The code is available at https://github.com/zht8506/UniHead.

Blind Image Quality Assessment (BIQA) aims to simulate human assessment of image quality. It has a great demand for labeled data, which is often insufficient in practice. Some researchers employ unsupervised methods to address this issue, which is challenging to emulate the human subjective system. To this end, we introduce a unified framework that combines semi-supervised and incremental learning to address the mentioned issue. Specifically, when training data is limited, semi-supervised learning is necessary to infer extensive unlabeled data. To facilitate semi-supervised learning, we use knowledge distillation to assign pseudo-labels to unlabeled data, preserving analytical capability. To gradually improve the quality of pseudo labels, we introduce incremental learning. However, incremental learning can lead to catastrophic forgetting. We employ Experience Replay by selecting representative samples during multiple rounds of semi-supervised learning, to alleviate forgetting and ensure model stability. Experimental results show that the proposed approach achieves state-of-the-art performance across various benchmark datasets. After being trained on the LIVE dataset, our method can be directly transferred to the CSIQ dataset. Compared with other methods, it significantly outperforms unsupervised methods on the CSIQ dataset with a marginal performance drop (-0.002) on the LIVE dataset. In conclusion, our proposed method demonstrates its potential to tackle the challenges in real-world production processes.

Language conditioned 3D object grounding aims to find the object within the 3D scene mentioned by natural language descriptions, which mainly depends on the matching between visual and natural language. Considerable improvement in grounding performance is achieved by improving the multimodal fusion mechanism or bridging the gap between detection and matching. However, several mismatches are ignored, i.e., mismatch in local visual representation and global sentence representation, and mismatch in visual space and corresponding label word space. In this paper, we propose crossmodal match for 3D grounding from mitigating these mismatches perspective. Specifically, to match local visual features with the global description sentence, we propose BEV (Bird’s-eye-view) based global information embedding module. It projects multiple object proposal features into the BEV and the relations of different objects are accessed by the visual transformer which can model both positions and features with long-range dependencies. To circumvent the mismatch in feature spaces of different modalities, we propose crossmodal consistency learning. It performs cross-modal consistency constraints to convert the visual feature space into the label word feature space resulting in easier matching. Besides, we introduce label distillation loss and global distillation loss to drive these matches learning in a distillation way. We evaluate our method in mainstream evaluation settings on three datasets, and the results demonstrate the effectiveness of the proposed method.

Storing intermediate frame segmentations as memory for long-range context modeling, spatial-temporal memory-based methods have recently showcased impressive results in semi-supervised video object segmentation (SVOS). However, these methods face two key limitations: 1) relying on non-local pixel-level matching to read memory, resulting in noisy retrieved features for segmentation; 2) segmenting each object independently without interaction. These shortcomings make the memory-based methods struggle in similar object and multi-object segmentation. To address these issues, we propose a query modulation method, termed QMVOS. This method summarizes object features into dynamic queries and then treats them as dynamic filters for mask prediction, thereby providing high-level descriptions and object-level perception for the model. Efficient and effective multi-object interactions are realized through inter-query attention. Extensive experiments demonstrate that our method can bring significant improvements to the memory-based SVOS method and achieve competitive performance on standard SVOS benchmarks. The code is available at https://github.com/zht8506/QMVOS.

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task’s features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model’s generalization ability. Extensive experiments conducted on eight standard IQA datasets have demonstrated the effectiveness of our proposed QFM-IQM.

Blind Image Quality Assessment (BIQA) mirrors subjective made by human observers. Generally, humans favor comparing relative qualities over predicting absolute qualities directly. However, current BIQA models focus on mining the "local" context, i.e., the relationship between information among individual images and the absolute quality of the image, ignoring the "global" context of the relative quality contrast among different images in the training data. In this paper, we present the Perceptual Context and Sensitivity BIQA (CSIQA), a novel contrastive learning paradigm that seamlessly integrates "global” and "local” perspectives into the BIQA. Specifically, the CSIQA comprises two primary components: 1) A Quality Context Contrastive Learning module, which is equipped with different contrastive learning strategies to effectively capture potential quality correlations in the global context of the dataset. 2) A Quality-aware Mask Attention Module, which employs the random mask to ensure the consistency with visual local sensitivity, thereby improving the model’s perception of local distortions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods.

Underwater image quality assessment (UIQA) plays a crucial role in monitoring and detecting the quality of acquired underwater images in underwater imaging systems. Currently, the investigation of UIQA encounters two major challenges. First, a lack of large-scale UIQA databases for benchmarking UIQA algorithms remains, which greatly restricts the development of UIQA research. The other limitation is that there is a shortage of effective UIQA methods that can faithfully predict underwater image quality. To alleviate these two challenges, in this paper, we first construct a large-scale UIQA database (UIQD). Specifically, UIQD contains a total of 5369 authentic underwater images that span abundant underwater scenes and typical quality degradation conditions. Extensive subjective experiments are executed to annotate the perceived quality of the underwater images in UIQD. Based on an in-depth analysis of underwater image characteristics, we further establish a novel baseline UIQA metric that integrates channel and spatial attention mechanisms and a transformer. Channel- and spatial attention modules are used to capture the image channel and local quality degradations, while the transformer module characterizes the image quality from a global perspective. Multilayer perception is employed to fuse the local and global feature representations and yield the image quality score. Extensive experiments conducted on UIQD demonstrate that the proposed UIQA model achieves superior prediction performance compared with the state-of-the-art UIQA and IQA methods. The proposed UIQD and UIQA models will be released athttps://github.com/YT2015?tab=repositories.

Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data. More specifically, we consider the traditional fine-tuning in BIQA as an interpretation of the pre-trained model. In this way, we further introduce a Transformer decoder to refine the perceptual information of the CLS token from different perspectives. This enables our model to establish the quality-aware feature manifold efficiently while attaining a strong generalization capability. Meanwhile, inspired by the subjective evaluation behaviors of human, we introduce a novel attention panel mechanism, which improves the model performance and reduces the prediction uncertainty simultaneously. The proposed BIQA method maintains a light-weight design with only one layer of the decoder, yet extensive experiments on eight standard BIQA datasets (both synthetic and authentic) demonstrate its superior performance to the state-of-the-art BIQA methods, i.e., achieving the SRCC values of 0.875 (vs. 0.859 in LIVEC) and 0.980 (vs. 0.969 in LIVE). Checkpoints, logs and code will be available at https://github.com/narthchin/DEIQT.

Interactive devices with touch screen have become commonly used in various aspects of daily life, which raises the demand for high production quality of touch screen glass. While it is desirable to develop effective defect detection technologies to optimize the automatic touch screen production lines, the development of these technologies suffers from the lack of publicly available datasets. To address this issue, we in this paper propose a dedicated touch screen glass defect dataset which includes seven types of defects and consists of 2504 images captured in various scenarios. All data are captured with professional acquisition equipment on the fixed workstation. Additionally, we benchmark the CNN- and Transformer-based object detection frameworks on the proposed dataset to demonstrate the challenges of defect detection on high-resolution images. Dataset and related code will be available at https://github.com/VincentHancoder/SSGD.

Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging complementary information from images captured by different sensors. Among these algorithms, deep unfolding network (DUN)-based methods achieve promising performance but still suffer from two issues: they lack a degradation-resistant-oriented fusion model and struggle to adequately consider the structural properties of DUNs, making them vulnerable to degradation scenarios. In this paper, we propose a Degradation-Resistant Unfolding Network (DeRUN) for the HIF task to generate high-quality fused images even in degradation scenarios. Specifically, we introduce a novel HIF model for degradation resistance and derive its optimization procedures. Then, we incorporate the optimization unfolding process into the proposed DeRUN for end-to-end training. To ensure the robustness and efficiency of DeRUN, we employ a joint constraint strategy and a lightweight partial weight sharing module. To train DeRUN, we further propose a gradient direction-based entropy loss with powerful texture representation capacity. Extensive experiments show that DeRUN significantly outperforms existing methods on four HIF tasks, as well as downstream applications, with cheaper computational and memory costs.

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.