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Omnidirectional image (OI) quality assessment is crucial to facilitate the development of virtual reality (VR) related technology. In this work, a distortion discrimination assisted multi-stream network is proposed for OI quality assessment. The multi-stream architecture is constructed by generating the viewport images received by the retina at one point to simulate the characteristics of humans perceiving VR contents. Additionally, the strategy of generating several viewport image sets from one OI is proposed for data augmentation. Furthermore, the facts that the human brain has the ability for both quality assessment and distortion type distinguishment, and the process of human brain handling two tasks exists information interaction inspire us to employ an auxiliary distortion discrimination task to facilitate the quality assessment task learning. Extensive experiments conducted on two public OI databases demonstrate the superiority of the proposed method to both traditional 2D quality metrics and existing metrics specific for OIs. Moreover, utilizing the assistant task is proven to be more effective than the single task learning for OI quality evaluation. Better generalization performance is also verified to be another valuable trait of the proposed method. [ABSTRACT FROM AUTHOR]

Many stereoscopic image retargeting (SIR) methods have been developed for automatically and intelligently resizing stereoscopic images and we cannot always rely on time-consuming subjective user studies to validate the performance of different SIR methods. It is therefore required to design reliable objective metrics for SIR quality evaluation. This paper extends our previous 2D aspect ratio similarity (ARS) metric to a stereo 3D version termed as StereoARS where the key idea is to investigate into retargeting inconsistency between the original stereo correspondences. Our proposed StereoARS operates via two stages: monocular quality estimation and binocular inconsistency detection. In the first stage, monocular quality estimation is performed by applying a modified ARS measure on the left and right views separately to quantify the quality degradation within each monocular view. In the second stage, binocular inconsistency detection is performed in both pixel-level and grid-level to characterize the influence of binocular rivalry and stereo visual discomfort on SIR quality. In addition, we also measure to what extent the original pixel visibility relation is preserved after SIR as another binocular quality factor. Finally, these monocular and binocular quality estimates are fused to produce an overall SIR quality score. Extensive experiments have demonstrated that StereoARS achieves better alignment with human subjective ratings than the existing metrics by a large margin. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of free-viewpoint video applications, virtual view synthesis has attracted extensive attention. In view synthesis, a new viewpoint is generated from the input color and depth images with a depth-image-based rendering (DIBR) algorithm. Current quality evaluation models for view synthesis typically operate on the synthesized images, i.e. after the DIBR process, which is computationally expensive. So a natural question is that can we infer the quality of DIBR-based synthesized images using the input color and depth images directly without performing the intricate DIBR operation. With this motivation, this paper presents a no-reference image quality prediction model for view synthesis via COlor-Depth Image Fusion, dubbed CODIF, where the actual DIBR is not needed. First, object boundary regions are detected from the color image, and a Wavelet-based image fusion method is proposed to imitate the interaction between color and depth images during the DIBR process. Then statistical features of the interactional regions and natural regions are extracted from the fused color-depth image to portray the influences of distortions in color/depth images on the quality of synthesized views. Finally, all statistical features are utilized to learn the quality prediction model for view synthesis. Extensive experiments on public view synthesis databases demonstrate the advantages of the proposed metric in predicting the quality of view synthesis, and it even suppresses the state-of-the-art post-DIBR view synthesis quality metrics. [ABSTRACT FROM AUTHOR]

In this article, we propose an efficient joint image quality assessment and enhancement algorithm for the 3-D-synthesized images using a global predictor, namely, kernel ridge regression (KRR). Recently, a few prediction-based image quality assessment (IQA) algorithms have been proposed for 3-D-synthesized images. These algorithms use efficient prediction algorithms and try to predict all the regions efficiently, except the boundaries of the regions with geometric distortions. Unfortunately, these algorithms only count the number of pixels along the boundaries of the regions with geometric distortions and subsequently, calculate the quality scores. With this view, we propose a new algorithm for 3-D-synthesized images based upon the global KRR-based predictor, which estimates the complete distortion surface with geometric distortions. Further, it uses the distortion surface to estimate the perceptual quality of the 3-D-synthesized images. Also, the joint quality assessment and enhancement algorithms for 3-D-synthesized images are missing in literature. With this view, we propose to estimate the distortion map of the geometric distortions via the same predictor used in quality estimation and it subsequently enhances the perceptual quality of the 3-D-synthesized images. The performance of the proposed quality assessment algorithm is better than the existing IQA algorithms. Also, the proposed quality enhancement algorithm is promising, significantly enhancing the perceptual quality of 3-D-synthesized images. [ABSTRACT FROM AUTHOR]

An ideal quality assessment model should simulate the properties of the visual brain to be consistent with human evaluation. The visual brain appears to have both evolved to seek an efficient, decorrelated representation of image information and to “match” the statistics of the natural image. On one hand, the theoretical studies suggest that sparse representation resembles the strategy in the primary visual cortex of brain for representing natural images. On the other hand, the natural scene statistics have driven the evolution of human visual system and have also inspired the understanding and simulating of visual perception. Inspired by these observations, in this paper, we propose a novel reduced-reference stereoscopic image quality assessment metric using sparse representation and natural scene statistics to simulate the visual perception of the brain. Specifically, the distribution statistics of the classified visual primitives extracted by sparse representation are used to measure the visual information, which is closely related to the hierarchical progressive process of human visual perception. Particularly, the mutual information of classified primitives between two view images is derived as a binocular cue to simulate the binocular fusion process. The maximum mechanism that is applied to select the visual information is a pooling mechanism with which complex cells use the maximal stimuli from a group of simple cells during the transfer process in the primary visual cortex. The natural scene statistics of locally normalized luminance coefficients are used to evaluate the natural losses due to the presence of distortions. The differences of the visual information and the natural scene statistics between the original and distorted images are used to compute the quality score by a prediction function which is trained using support vector regression. Experimental results show that the proposed metric outperforms the state-of-the-art stereoscopic image quality assessment metrics on LIVE 3D IQA database and NBU-MDSID Phase-II database, and delivers competitive performance on Waterloo IVC 3D database. [ABSTRACT FROM AUTHOR]

Free viewpoint video (FVV) has received considerable attention owing to its widespread applications in several areas such as immersive entertainment, remote surveillance and distanced education. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the “blind” environment (without reference images), a real-time and reliable blind quality assessment metric is urgently required. However, the existing image quality assessment metrics are insensitive to the geometric distortions engendered by DIBR. In this research, a novel blind method of DIBR-synthesized images is proposed based on measuring geometric distortion, global sharpness and image complexity. First, a DIBR-synthesized image is decomposed into wavelet subbands by using discrete wavelet transform. Then, the Canny operator is employed to detect the edges of the binarized low-frequency subband and high-frequency subbands. The edge similarities between the binarized low-frequency subband and high-frequency subbands are further computed to quantify geometric distortions in DIBR-synthesized images. Second, the log-energies of wavelet subbands are calculated to evaluate global sharpness in DIBR-synthesized images. Third, a hybrid filter combining the autoregressive and bilateral filters is adopted to compute image complexity. Finally, the overall quality score is derived to normalize geometric distortion and global sharpness by the image complexity. Experiments show that our proposed quality method is superior to the competing reference-free state-of-the-art DIBR-synthesized image quality models. [ABSTRACT FROM AUTHOR]

Most existing blind image quality assessment (BIQA) methods belong to supervised methods, which always need a large number of image samples and expensive subjective scores for training a quality prediction model. In this paper, we focus our attention on the unsupervised BIQA methods and put forward a novel unsupervised approach. The main idea of our method is to quantify the image quality degradation through measuring the structure, naturalness, and the perception quality variations of the distorted image from the pristine natural images. In specific, the structure variation is captured by the deviations of the image phase congruency and gradients distributions. The naturalness variation is characterized through the distributions variations of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Compared with existing unsupervised methods, we initiatively introduce the perception quality measurement into the construction of unsupervised BIQA method, which is conducted by characterizing the prediction discrepancy between the image and its brain prediction based on the free-energy principle in the newly revealed brain theory. After feature extraction, we learn a pristine multivariate Gaussian (MVG) model with the extracted features from a set of pristine natural images. The quality of a new image is finally defined as the distance between its MVG model and the learned pristine MVG model. The extensive experiments conducted on LIVE, TID2013, CSIQ, Toyama, CID2013, and the Waterloo Exploration databases demonstrate that the proposed method achieves comparative prediction performance with the state-of-the-art BIQA methods. [ABSTRACT FROM AUTHOR]

This paper proposes to blindly evaluate the quality of images synthesized via a depth image-based rendering (DIBR) procedure. As a significant branch of virtual reality (VR), superior DIBR techniques provide free viewpoints in many real applications, including remote surveillance and education; however, limited efforts have been made to measure the performance of DIBR techniques, or equivalently the quality of DIBR-synthesized views, especially in the condition when references are unavailable. To achieve this aim, we develop a novel blind image quality assessment (IQA) method via multiscale natural scene statistical analysis (MNSS). The design principle of our proposed MNSS metric is based on two new natural scene statistics (NSS) models specific to the DBIR-synthesized IQA. First, the DIBR-introduced geometric distortions damage the local self-similarity characteristic of natural images, and the damage degrees of self-similarity present particular variations at different scales. Systematically combining the measurements of the variations mentioned above can gauge the naturalness of the input image and thus indirectly reflect the quality changes of images generated using different DIBR methods. Second, it was found that the degradations in main structures of natural images at different scales remain almost the same, whereas the statistical regularity is destroyed in the DIBR-synthesized views. Estimating the deviation of degradations in main structures at different scales between one DIBR-synthesized image and the statistical model, which is constructed based on a large number of natural images, can quantify how a DIBR method damages the main structures and thus infer the image quality. Via trials, the two NSS-based features extracted above can well predict the quality of DIBR-synthesized images. Further, the two features come from distinct points of view, and we hence integrate them via a straightforward multiplication to derive the proposed blind MNSS metric, which achieves better performance than each component and state-of-the-art quality methods. [ABSTRACT FROM AUTHOR]

360-degree images/videos have been dramatically increasing in recent years. The characteristic of omnidirectional-view results in high resolution of 360-degree images/videos, which makes them difficult to be transported and stored. To deal with the problem, video coding technologies are used to compress the omnidirectional content but they will introduce the compression distortion. Therefore, it is important to study how popular coding technologies affect the quality of 360-degree images. In this paper, we present a study on both subjective and objective quality assessment of compressed virtual reality (VR) images. We first build a compressed VR image quality (CVIQ) database including 16 reference images and 528 compressed ones with three prevailing coding technologies. Then, we propose a multi-channel convolution neural network (CNN) for blind 360-degree image quality assessment (MC360IQA). To be consistent with the visual content seen in the VR device, we project each 360-degree image into six viewport images, which are adopted as inputs of the proposed model. MC360IQA consists of two parts, a multi-channel CNN and an image quality regressor. The multi-channel CNN includes six parallel hyper-ResNet34 networks, where the hyper structure is used to incorporate the features from intermediate layers. The image quality regressor fuses the features and regresses them to final scores. The experimental results show that our model achieves the best performance among the state-of-art full-reference (FR) and no-reference (NR) image quality assessment (IQA) models on the CVIQ database and other available 360-degree IQA database. [ABSTRACT FROM AUTHOR]

In past years, various encrypted algorithms have been proposed to fully or partially protect the multimedia content in view of practical applications. In the context of digital TV broadcasting, transparent encryption only protects partial content and fulfills both security and quality requirements. To date, only a few reference-based works have been reported to evaluate the quality of transparently encrypted images. However, these works are incapable of reference-unavailable conditions. In this paper, we conduct the first attempt that proposes a novel quality evaluator in the absence of reference images. The key strategy of the proposed metric lies in extracting features by considering the motivation of transparently encrypted images. Specifically, given that encrypted images prevent content from being easily recognized, several features, including correlation coefficient, information entropy, and intensity statistic, are preliminarily extracted to estimate visual recognizability. Meanwhile, considering that encrypted images are avoided since they are of extremely low quality, we also capture many features to measure the distortions on multiple quality-sensitive image attributes, such as naturalness, structure, and texture. Finally, the quality evaluator is built by bridging all extracted features and corresponding quality scores via a regression module. Experimental results demonstrate that the proposed method is superior to the mainstream no-reference quality evaluation methods designed for synthetically distorted images and possesses a close approximation to state-of-the-art reference-based methods designed for encrypted images. [ABSTRACT FROM AUTHOR]

With multitudes of image processing applications, image quality assessment (IQA) has become a prerequisite for obtaining maximally distinctive statistics from images. Despite the widespread research in this domain over several years, existing IQA algorithms have a number of key limitations concerning different image distortion types and algorithms’ computational efficiency. Images that are synthesized using depth image-based rendering have applications in various disciplines, such as free viewpoint videos, which enable synthesis of novel realistic images in the referenceless environment. In the literature, very few no-reference (NR) quality assessment metrics of three-dimensional (3-D) synthesized images are proposed, and most of them are computationally expensive, which makes it difficult for them to be deployed in real-time applications. In this paper, we attribute the geometrically distorted pixels as outliers in 3-D synthesized images. This assumption is validated using the three $sigma$ rule-based robust outlyingness ratio. We propose a novel fast and accurate blind IQA metric of 3-D synthesized images using nonlinear median filtering since the median filtering has the capability of identifying and removing outliers. The advantages of the proposed algorithm are twofold. First, it uses a simple technique, i.e., median filtering, to capture the level of geometric and structural distortions (up to some extend). Second, the proposed algorithm has higher computational efficiency. Experiments show the superiority of the proposed NR IQA algorithm over existing state-of-the-art full-, reduced-, and NR IQA methods, in terms of both predicting accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

Objective image quality assessment employs mathematical and computational theory to objectively assess the quality of output images based on the human visual system (HVS). In this paper, a novel approach based on multifeature extraction in the spatial and frequency domains is proposed. We combine the gradient magnitude and phase congruency maps to generate a local structure (LS) map, which can perceive local structural distortions. The LS matches well with HVS and highlights differences with details. For complex visual information, such as texture and contrast sensitivity, we deploy the log-Gabor filter, and spatial frequency, respectively, to effectively capture their variations. Moreover, we employ the random forest (RF) to overcome the limitations of existing pooling methods. Compared with support vector regression, RF can obtain better prediction results. Extensive experimental results on the five benchmark databases indicate that the proposed method precedes all the state-of-the-art image quality assessment metrics in terms of prediction accuracy. In addition, the proposed method is in compliance with the subjective evaluations. [ABSTRACT FROM AUTHOR]

Camera images in reality are easily affected by various distortions, such as blur, noise, blockiness, and the like, which damage the quality of images. The complexity of distortions in camera images raises significant challenge for precisely predicting their perceptual quality. In this paper, we present an image quality assessment (IQA) approach that aims to solve this challenging problem to some extent. In the proposed method, we first extract the low-level and high-level statistical features, which can capture the quality degradations effectively. On the one hand, the first kind of statistical features are extracted from the locally mean subtracted and contrast normalized coefficients, which denote the low-level features in the early human vision. On the other hand, the recently proposed brain theory and neuroscience, especially the free-energy principle, reveal that the human brain tries to explain its encountered visual scenes through an inner creative model, with which the brain can produce the projection for the image. Then, the quality of perceptions can be reflected by the divergence between the image and its brain projection. Based on this, we extract the second type of features from the brain perception mechanism, which represent the high-level features. The low-level and high-level statistical features can play a complementary role in quality prediction. After feature extraction, we design a neural network to integrate all the features and convert them to the final quality score. Extensive tests performed on two real camera image datasets prove the validity of our method and its advantageous predicting ability over the competitive IQA models. [ABSTRACT FROM AUTHOR]

The rapid growth of ocean exploration has led to an increase in the demand of sonar images, since they contain a great amount of information collected by sonar systems such as distributions of underwater creatures and minerals. For further analysis, sonar images usually need to be transmitted via underwater acoustic channel (UAC). However, the hostile nature of UAC makes the transmitted sonar images have high probability to be afflicted with distortions introduced in transmission procedures. To monitor image quality, a partial-reference sonar image quality predictor (PSIQP) is proposed in this paper. A unique property that differentiates the proposed PSIQP metric from existing works is the consideration of the viewing behavior of the human visual system for sonar images, which motivates us to take both image perception and understanding into consideration. The results of experiments based on sonar image quality database show that the proposed metric provides accurate predictions across a variety of compression and transmission distortions. [ABSTRACT FROM AUTHOR]

In the past decade, extensive image quality metrics have been proposed. The majority of them are tailored for the images that contain a specific type of distortion. However, in practice, the images are usually degraded by different types of distortions simultaneously. This poses great challenges to the existing quality metrics. Motivated by this, this paper proposes a no-reference quality index for the multiply distorted images using the biorder structure degradation and the nonlocal statistics. The design philosophy is inspired by the fact that the human visual system (HVS) is highly sensitive to the degradations of both the spatial contrast and the spatial distribution, which are prone to be changed by the joint effects of the multiple distortions. Specifically, the multiresolution representation of the image is first built by downsampling to simulate the hierarchical property of the HVS. Then, the structure degradation is calculated to measure the spatial contrast. Considering the fact that the human visual cortex has the separate mechanisms to perceive the first- and second-order structures, dubbed biorder structures, the degradations of biorder structures are calculated to account for the spatial contrast, producing the first group of the quality-aware features. Furthermore, the nonlocal self-similarity statistics is calculated to measure the spatial distribution, producing the second group of features. Finally, all the features are fed into the random forest regression model to learn the quality model for the multiply distorted images. Extensive experimental results conducted on the three public databases demonstrate the superiority of the proposed metric to the state-of-the-art metrics. Moreover, the proposed metric is also advantageous over the existing metrics in terms of the generalization ability. [ABSTRACT FROM AUTHOR]

Perceptual image quality assessment (IQA) plays an important role in numerous applications, including image restoration, compression, enhancement, and others. Although many works have been conducted on individually distorted IQA problems and have achieved encouraging results, few studies have been conducted on multiple distorted (MD) IQA problems. Thus, limited progress has been made. In this paper, we propose a novel no reference image quality assessment (NR-IQA) method, named improved multiscale local binary pattern (IMLBP), for addressing multiply distorted IQA problems. The image structures are sensitive to image distortions, which motivates us to utilize the structural characteristics for overall image quality prediction. We improved the local binary pattern (LBP) by considering the human visual mechanism to better extract the structural information. The IMLBP contains two parts, the LBP and the radius difference LBP (DLBP). The DLBP reflects the values’ changes in the radial direction. Specifically, when the radius value is small, the proposed descriptor is computed to represent microstructural information. Conversely, it represents macrostructural information when the radius becomes large. Moreover, to better mimick the human visual mechanism, the IMLBP is computed with the multiscale strategy and the operation is based on a patch unit whose size is proportional to the radius value. The frequency histogram of feature maps is transformed to feature vectors. Subsequently, a predictable function trained by the support vector regression is used to infer the overall quality score. Experimental results show that the proposed method outperforms most state-of-the-art IQA metrics on publicly available multiply distorted image databases. [ABSTRACT FROM AUTHOR]

Traditional full-reference image quality assessment (IQA) metrics generally predict the quality of the distorted image by measuring its deviation from a perfect quality image called reference image. When the reference image is not fully available, the reduced-reference and no-reference IQA metrics may still be able to derive some characteristics of the perfect quality images, and then measure the distorted image's deviation from these characteristics. In this paper, contrary to the conventional IQA metrics, we utilize a new “reference” called pseudo-reference image (PRI) and a PRI-based blind IQA (BIQA) framework. Different from a traditional reference image, which is assumed to have a perfect quality, PRI is generated from the distorted image and is assumed to suffer from the severest distortion for a given application. Based on the PRI-based BIQA framework, we develop distortion-specific metrics to estimate blockiness, sharpness, and noisiness. The PRI-based metrics calculate the similarity between the distorted image's and the PRI's structures. An image suffering from severer distortion has a higher degree of similarity with the corresponding PRI. Through a two-stage quality regression after a distortion identification framework, we then integrate the PRI-based distortion-specific metrics into a general-purpose BIQA method named blind PRI-based (BPRI) metric. The BPRI metric is opinion-unaware (OU) and almost training-free except for the distortion identification process. Comparative studies on five large IQA databases show that the proposed BPRI model is comparable to the state-of-the-art opinion-aware- and OU-BIQA models. Furthermore, BPRI not only performs well on natural scene images, but also is applicable to screen content images. The MATLAB source code of BPRI and other PRI-based distortion-specific metrics will be publicly available. [ABSTRACT FROM AUTHOR]

State-of-the-art algorithms for blind image quality assessment (BIQA) typically have two categories. The first category approaches extract natural scene statistics (NSS) as features based on the statistical regularity of natural images. The second category approaches extract features by feature encoding with respect to a learned codebook. However, several problems need to be addressed in existing codebook-based BIQA methods. First, the high-dimensional codebook-based features are memory-consuming and have the risk of over-fitting. Second, there is a semantic gap between the constructed codebook by unsupervised learning and image quality. To address these problems, we propose a novel codebook-based BIQA method by optimizing multistage discriminative dictionaries (MSDDs). To be specific, MSDDs are learned by performing the label consistent K-SVD (LC-KSVD) algorithm in a stage-by-stage manner. For each stage, a new quality consistency constraint called “quality-discriminative regularization” term is introduced and incorporated into the reconstruction error term to form a unified objective function, which can be effectively solved by LC-KSVD for discriminative dictionary learning. Then, the latter stage takes the reconstruction residual data in the former stage as input based on which LC-KSVD is repeatedly performed until the final stage is reached. Once the MSDDs are learned, multistage feature encoding is performed to extract feature codes. Finally, the feature codes are concatenated across all stages and aggregated over the entire image for quality prediction via regression. The proposed method has been evaluated on five databases and experimental results well confirm its superiority over existing relevant BIQA methods. [ABSTRACT FROM AUTHOR]

Traditional blind image quality assessment (IQA) measures generally predict quality from a sole distorted image directly. In this paper, we first introduce multiple pseudo reference images (MPRIs) by further degrading the distorted image in several ways and to certain degrees, and then compare the similarities between the distorted image and the MPRIs. Via such distortion aggravation, we can have some references to compare with, i.e., the MPRIs, and utilize the full-reference IQA framework to compute the quality. Specifically, we apply four types and five levels of distortion aggravation to deal with the commonly encountered distortions. Local binary pattern features are extracted to describe the similarities between the distorted image and the MPRIs. The similarity scores are then utilized to estimate the overall quality. More similar to a specific pseudo reference image (PRI) indicates closer quality to this PRI. Owning to the availability of the created multiple PRIs, we can reduce the influence of image content, and infer the image quality more accurately and consistently. Validation is conducted on four mainstream natural scene image and screen content image quality assessment databases, and the proposed method is comparable to or outperforms the state-of-the-art blind IQA measures. The MATLAB source code of the proposed measure will be publicly available. [ABSTRACT FROM AUTHOR]

Depth-image-based rendering (DIBR) is a fundamental technique in free viewpoint video, which is widely adopted to synthesize virtual viewpoints. The warping and rendering operations in DIBR generally introduce geometric distortions and sharpness change. The state-of-the-art quality indices are limited in dealing with such images since they are sensitive to geometric changes. In this paper, a new quality model for DIBR-synthesized view images is presented by measuring LOcal Geometric distortions in disoccluded regions and global Sharpness (LOGS). A disoccluded region detection method is first proposed using SIFT-flow-based warping. Then, the sizes and distortion strength of local disoccluded regions are combined to generate a score. Furthermore, a reblurring-based strategy is proposed to quantify the global sharpness. Finally, the overall quality score is calculated by pooling the scores of local disoccluded regions and global sharpness. Experiments on four public DIBR-synthesized image/video databases show the superiority of the proposed metric over the state-of-the-art quality models. The proposed method is further adopted for boosting the performances of existing quality metrics and benchmarking DIBR algorithms, both achieving very promising results. [ABSTRACT FROM PUBLISHER]

In this paper, we address problems associated with free-energy-principle-based image quality assessment (IQA) algorithms for objectively assessing the quality of Screen Content (SC) and three-dimensional (3-D) synthesized images and also propose a very fast and efficient IQA algorithm to address these issues. These algorithms separate an image into predicted and disorder residual parts and assume disorder residual part does not contribute much to the overall perceptual quality. These algorithms fail for quality estimation of SC images as information of textual regions in SC images are largely separated into the disorder residual part and less information in the predicted part and subsequently, given a negligible emphasis. However, this is in contrast with the characteristics of human vision. Since our eyes are well trained to detect text in daily life. So, our human vision has prior information about text regions and can sense small distortions in these regions. In this paper, we proposed a new reduced-reference IQA algorithm for SC images based upon a more perceptually relevant prediction model and distortion categorization, which overcomes problems with existing free-energy-principle-based predictors. From experiments, it is validated that the proposed model has a better capability of efficiently estimating the quality of SC images as compared to the recently developed reduced-reference IQA algorithms. We also applied the proposed algorithm to judge the quality of 3-D synthesized images and observed that it even achieves better performance than the full-reference IQA metrics specifically designed for the 3-D synthesized views. [ABSTRACT FROM PUBLISHER]

Screen content image (SCI) has recently emerged as an active topic due to the rapidly increasing demand in many graphically rich services such as wireless displays and virtual desktops. SCIs are often composed of pictorial regions and computer generated textual/graphical content, which exhibit different statistical properties that often lead to different viewer behaviors. Inspired by this, we propose an objective quality assessment approach for SCIs that incorporates both visual field adaptation and information content weighting into structural similarity based local quality assessment. Furthermore, we develop a perceptual screen content coding scheme based on the newly proposed quality assessment measure, targeting at further improving the SCI compression performance. Experimental results show that the proposed quality assessment method not only better predicts the perceptual quality of SCIs, but also demonstrates great potentials in the design of perceptually optimal SCI compression schemes. [ABSTRACT FROM PUBLISHER]

In image/video systems, contrast adjustment which manages to enhance visual quality is nowadays an important research topic. Yet very limited struggles have been made to the exploration of visual quality assessment for contrast adjustment. To tackle the issue, this paper proposes a novel reduced-reference (RR) quality metric with the integration of bottom-up and top-down strategies. The former one stems from the recently revealed free energy principle that tells that the human visual system seeks to comprehend an input image via uncertainty removal, while the latter one is toward using the symmetric Kullback–Leibler divergence to compare the histogram of the contrast-altered image with that of the pristine image. The bottom-up and top-down strategies are lastly incorporated to derive the RR contrast-altered image quality measure. A comparison using numerous existing IQA models is carried out on five contrast related databases/subsets in CID2013, CCID2014, CSIQ, TID2008, and TID2013, and experimental results validate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) has been an active research area during last decades. Many existing objective IQA models share a similar two-step structure with measuring local distortion before pooling. Compared with the rapid development for local distortion measurement, seldom effort has been made dedicated to effective pooling schemes. In this paper, we design a new pooling model via the analysis of distortion distribution affected by image content and distortion. That is, distributions of distortion position, distortion intensity, frequency changes, and histogram changes are comprehensively considered to infer an overall quality score. Experimental results conducted on four large-scale image quality databases (LIVE, TID2008, CSIQ, and CCID2014) concluded with three valuable findings. First, the proposed technique leads to consistent improvement in the IQA performance for studied local distortion measures. Second, relative to the traditional pooling, the performance gain of our algorithm is beyond 15% on average. Third, the best overall performance made by the proposed strategy outperforms state-of-the-art competitors. [ABSTRACT FROM PUBLISHER]

With the widespread adoption of multidevice communication, such as telecommuting, screen content images (SCIs) have become more closely and frequently related to our daily lives. For SCIs, the tasks of accurate visual quality assessment, high-efficiency compression, and suitable contrast enhancement have thus currently attracted increased attention. In particular, the quality evaluation of SCIs is important due to its good ability for instruction and optimization in various processing systems. Hence, in this paper, we develop a new objective metric for research on perceptual quality assessment of distorted SCIs. Compared to the classical MSE, our method, which mainly relies on simple convolution operators, first highlights the degradations in structures caused by different types of distortions and then detects salient areas where the distortions usually attract more attention. A comparison of our algorithm with the most popular and state-of-the-art quality measures is performed on two new SCI databases (SIQAD and SCD). Extensive results are provided to verify the superiority and efficiency of the proposed IQA technique. [ABSTRACT FROM PUBLISHER]