Blind Quality Research Articles

Blind Quality Index for Super Resolution Reconstructed Images Using First- and Second-Order Structural Degradation

Blind Quality Index for Super Resolution Reconstructed Images Using First- and Second-Order Structural Degradation

3D visual saliency and convolutional neural network for blind mesh quality assessment

A number of full reference and reduced reference methods have been proposed in order to estimate the perceived visual quality of 3D meshes. However, in most practical situations, there is a limited access to the information related to the reference and the distortion type. For these reasons, the development of a no-reference mesh visual quality (MVQ) approach is a critical issue, and more emphasis needs to be devoted to blind methods. In this work, we propose a no-reference convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes. The method is called SCNN-BMQA (3D visual saliency and CNN for blind mesh quality assessment). The main contribution is the usage of a CNN and 3D visual saliency to estimate the perceived visual quality of distorted meshes. To do so, the CNN architecture is fed by small patches selected carefully according to their level of saliency. First, the visual saliency of the 3D mesh is computed. Afterward, we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Then the obtained views are split into 2D small patches that pass through a saliency filter in order to select the most relevant patches. Finally, a CNN is used for the feature learning and the quality score estimation. Extensive experiments are conducted on four prominent MVQ assessment databases, including several tests to study the effect of the CNN parameters, the effect of visual saliency and comparison with existing methods. Results show that the trained CNN achieves good rates in terms of correlation with human judgment and outperforms the most effective state-of-the-art methods.

Blind Quality Metric of DIBR-Synthesized Images in the Discrete Wavelet Transform Domain

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.

Learning to Measure Stereoscopic S3D Image Perceptual Quality on the Basis of Binocular Rivalry Response

Blind perceptual quality measurement of stereoscopic 3D (S3D) images has become an important and challenging issue in the research field of S3D imaging. In this paper, a blind S3D image quality measurement (IQM) method that does not depend on examples of distorted S3D images and corresponding subjective scores is proposed. As the main contribution of this work, we replace human subjective scores with a quality codebook of binocular rivalry responses (BRRs); this allows blind S3D-IQM methods to be learned without evaluation performance loss. Our results, using the publicly accessible LIVE S3D dataset, confirm that our method is highly robust and efficient.

Implementation of a Blind Quality Control Program in Blood Alcohol Analysis.

Declared proficiency tests are limited in their use for testing the performance of the entire system, because analysts are aware that they are being tested. A blind quality control (BQC) is intended to appear as a real case to the analyst to remove any intentional or subconscious bias. A BQC program allows a real-time assessment of the laboratory's policies and procedures and monitors reliability of casework. In September 2015, the Houston Forensic Science Center (HFSC) began a BQC program in blood alcohol analysis. Between September 2015 and July 2018, HFSC submitted 317 blind cases: 89 negative samples and 228 positive samples at five target concentrations (0.08, 0.15, 0.16, 0.20 and 0.25g/100mL; theoretical targets). These blood samples were analyzed by a headspace gas chromatograph interfaced with dual-flame ionization detectors (HS-GC-FID). All negative samples produced `no ethanol detected' results. The mean (range) of reported blood alcohol concentrations (BACs) for the aforementioned target concentrations was 0.075 (0.073-0.078), 0.144 (0.140-0.148), 0.157 (0.155-0.160), 0.195 (0.192-0.200) and 0.249 (0.242-0.258) g/100mL, respectively. The average BAC percent differences from the target for the positive blind cases ranged from -0.4 to -6.3%, within our uncertainty of measurement (8.95-9.18%). The rate of alcohol evaporation/degradation was determined negligible. A multiple linear regression analysis was performed to compare the % difference in BAC among five target concentrations, eight analysts, three HS-GC-FID instruments and two pipettes. The variables other than target concentrations showed no significant difference (P > 0.2). While the 0.08g/100mL target showed a significantly larger % difference than higher target concentrations (0.15-0.25g/100mL), the % differences among the higher targets were not concentration-dependent. Despite difficulties like gaining buy-in from stakeholders and mimicking evidence samples, the implementation of a BQC program has improved processes, shown methods are reliable and added confidence to staff's testimony in court.

Blind Quality Assessment for Cartoon Images

Current blind image quality assessment (BIQA) algorithms are mainly designed for natural images. Unfortunately, cartoon and cartoon-like images are quite different from natural images. Hence, recent BIQA methods are not very robust to cartoon images. In this paper, we propose a specific BIQA algorithm designed for cartoon images, which consists of the following terms. First, a cartoon image is divided into edge areas and nonedge areas via a Tchebichef moment (TM)-based process. Second, a multiorder sharpness statistic term is used to measure the quality of the edges, and a sharpness statistic prior model of high-quality (HQ) cartoon images is built. Finally, a local encoding statistic term is adopted to describe the textural complexity in the nonedge areas, and a texture statistic prior model is also established. The experimental results on the cartoon image datasets demonstrate that the proposed method can accurately evaluate the visual quality of cartoon images and is more suitable for cartoon scenarios than some traditional BIQA algorithms.

Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy

Due to the 3D spatiotemporal regularities of natural videos and small-scale video quality databases, effective objective video quality assessment (VQA) metrics are difficult to obtain but highly desirable. In this paper, we propose a general-purpose no-reference VQA framework that is based on weakly supervised learning with a convolutional neural network (CNN) and a resampling strategy. First, an eight-layer CNN is trained by weakly supervised learning to construct the relationship between the deformations of the 3D discrete cosine transform of video blocks and the corresponding weak labels judged by a full-reference (FR) VQA metric. Thus, the CNN obtains the quality assessment capacity converted from the FR-VQA metric, and the effective features of the distorted videos can be extracted through the trained network. Then, we map the frequency histogram calculated from the quality score vectors predicted by the trained network onto the perceptual quality. Especially, to improve the performance of the mapping function, we transfer the frequency histogram of the distorted images and videos to resample the training set. The experiments are carried out on several widely used VQA databases. The experimental results demonstrate that the proposed method is on a par with some state-of-the-art VQA metrics and has promising robustness.

Effective and Efficient Blind Quality Evaluator for Contrast Distorted Images

This paper mainly focuses on developing a blind quality assessment method that can effectively and efficiently evaluate the quality of contrast distorted images without requiring reference information. Through experiments, we discover and validate that the global intensity change is the main characteristic of contrast distorted images and has a close relationship to the perceptual quality. With these observations, two elements are utilized to quantify this characteristic, i.e., the maximum information entropy of intensity values and the Kullback-Leibler (K-L) divergence between the test image's intensity histogram and the prior one based on the statistical experiment over a great number of high-quality images. To be specific, the entropy represents the valuable information of an image and the K-L divergence reflects the change degree of intensity distribution. In view of these, the proposed method is generated by combining these two elements linearly. Extensive experiments on three publicly available databases demonstrate the superiority of the proposed method. More specifically, it is more consistent with subjective evaluation results than the state-of-the-art image quality assessment methods and requires a lower computational complexity.

Blind Quality Assessment of 3-D Synthesized Views Based on Hybrid Feature Classes

In this paper, a novel quality metric to evaluate depth-based synthesized views is proposed. This metric relies on a hybrid approach that uses features extracted in different phases of the image synthesis procedure, namely from the bitstream, from intermediate data produced during the synthesis process, and from the final synthesized view; these features are then combined using support vector regression. A new data set of synthesized images, with compression and rendering artifacts, was built and used to develop and assess the proposed metric. The metric performance is compared with conventional full-reference two-dimensional image quality assessment metrics and with quality assessment metrics developed specifically for synthesized images. The experimental results showed that the proposed solution outperforms the considered benchmark metrics, being able to predict the subjective quality scores of the synthesized images with a Pearson correlation coefficient close to 0.9.

Fast Blind Quality Assessment of DIBR-Synthesized Video Based on High-High Wavelet Subband.

Free-viewpoint video, as the development direction of the next-generation video technologies, uses the depth-image-based rendering (DIBR) technique for the synthesis of video sequences at viewpoints, where real captured videos are missing. As reference videos at multiple viewpoints are not available, a blind reliable real-time quality metric of the synthesized video is needed. Although no-reference quality metrics dedicated to synthesized views successfully evaluate synthesized images, they are not that effective when evaluating synthesized video due to additional temporal flicker distortion typical only for video. In this paper, a new fast no-reference quality metric of synthesized video with synthesis distortions is proposed. It is guided by the fact that the DIBR-synthesized images are characterized by increased high frequency content. The metric is designed under the assumption that the perceived quality of DIBR-synthesized video can be estimated by quantifying the selected areas in the high-high wavelet subband. The threshold is used to select the most important distortion sensitive regions. The proposed No-Reference Morphological Wavelet with Threshold (NR_MWT) metric is computationally extremely efficient, comparable to PSNR, as the morphological wavelet transformation uses very short filters and only integer arithmetic. It is completely blind, without using machine learning techniques. Tested on the publicly available dataset of synthesized video sequences characterized by synthesis distortions, the metric achieves better performances and higher computational efficiency than the state-of-the-art metrics dedicated to DIBR-synthesized images and videos.

Remote-Sensing Image Usability Assessment Based on ResNet by Combining Edge and Texture Maps

Authentic remote-sensing images suffer non-uniform complex distortions during acquisition, transmission, and storage. Clouds, light, and exposure also affect local quality. This paper constructs a usability-based subjective remote-sensing image dataset and gives a definition of usability for images with non-uniform distortion, where the image usability is determined by the weighted quality of image's blocks. It is difficult to extract the handcraft features from remote-sensing images with complex mixture distortion. Recently, convolutional neural network (CNN) has been introduced into blind quality assessment for images with uniform distortion, which includes feature learning and regression in one processing. In this paper, we first describe and systematically analyze the usability of remote-sensing images in detail. Then, we propose a remote-sensing image usability assessment (RSIUA) method based on a residual network by combining edge and texture maps. The score of remote-sensing image usability was obtained with the weighted averaging of the quality scores of all image blocks, and the weight of each image block was determined by its quality score. We compared the proposed method with three traditional image quality assessment methods, one CNN-based method for images with simulated distortion, and one scale-invariant feature transform-based RSIUA method. The linear correlation coefficient, Spearman's rank ordered correlation coefficient, and root-mean-squared error of experiments demonstrate that our method outperforms all five competitors. The experiments also reveal that the edge and texture maps can improve the performance.

Study of Subjective Quality and Objective Blind Quality Prediction of Stereoscopic Videos.

We present a new subjective and objective study on full high-definition (HD) stereoscopic (3D or S3D) video quality. In subjective study, we constructed an S3D video dataset with 12 pristine and 288 test videos, and the test videos are generated by applying the H.264 and H.265 compression, blur and frame freeze artifacts. We also propose a no reference (NR) objective video quality assessment (QA) algorithm that relies on measurements of the statistical dependencies between the motion and disparity subband coefficients of S3D videos. Inspired by the Generalized Gaussian Distribution (GGD) approach in liu2011statistical, we model the joint statistical dependencies between the motion and disparity components as following a Bivariate Generalized Gaussian Distribution (BGGD). We estimate the BGGD model parameters (α,β) and the coherence measure (Ψ) from the eigenvalues of the sample covariance matrix (M) of the BGGD. In turn, we model the BGGD parameters of pristine S3D videos using a Multivariate Gaussian (MVG) distribution. The likelihood of a test video's MVG model parameters coming from the pristine MVG model is computed and shown to play a key role in the overall quality estimation. We also estimate the global motion content of each video by averaging the SSIM scores between pairs of successive video frames. To estimate the test S3D video's spatial quality, we apply the popular 2D NR unsupervised NIQE image QA model on a frame-by-frame basis on both views. The overall quality of a test S3D video is finally computed by pooling the test S3D video's likelihood estimates, global motion strength and spatial quality scores. The proposed algorithm, which is 'completely blind' (requiring no reference videos or training on subjective scores) is called the Motion and Disparity based 3D video quality evaluator (MoDi3D). We show that MoDi3D delivers competitive performance over a wide variety of datasets including the IRCCYN dataset, the WaterlooIVC Phase I dataset, the LFOVIA dataset and our proposed LFOVIAS3DPh2 S3D video dataset.

Blind Quality Assessment of Tone-Mapped Images Considering Colorfulness, Naturalness, and Structure

Nowadays, high-dynamic-range (HDR) imaging represents a prevailing trend and attracts much attention from both academic and industrial scholars. Since HDR images cannot be properly produced on the mainstream low-dynamic-range (LDR) displays, various tone-mapping operators or postprocessing technologies have been designed to transform HDR images into LDR images for visualization on LDR displays. However, it inevitably induces artifacts and distortions due to dynamic range compression. Besides, existing tone-mapped (TM) technologies cannot effectively handle all kinds of images with diverse contents and structures, leaving to a very challenging and urgent image quality assessment (IQA) problem. To cope with this challenge, in this paper, an effective blind quality assessment approach for TM images is proposed through a comprehensive consideration of their characteristics. More specifically, to dig out sufficient information from TM images, multiple quality-sensitive features are captured to fully represent different attributes, including colorfulness, naturalness, and structure. The connection between feature space and associated subjective ratings is established via a regression model. Extensive experiments on a recently released TM image database prove that the proposed approach is superior to the state-of-the-art no-reference IQA approaches.

Blind quality assessment metric and degradation classification for degraded document images

Epochal documents suffer from several types of noises that accumulate and evolve over time. This significantly affects their quality and makes their storage and the interpretation of their visual content problematic. Digital preservation seems the most viable and the most promising. Moreover, measuring the amount of degradation and quality assessment of degraded documents is highly desirable for applications such as selecting the proper algorithms for enhancement and analysis of document images, filtering the damaged images, tuning the processing algorithms parameters, document repairing, psychological study, etc. The first contribution of this work is the proposition of an efficient Multi-distortion Document Quality Measure (MDQM) for quality assessment of physically degraded document images. The proposed MDQM metric is based on three sets of spatial and frequency image features. These features are extracted from two layers of text and non-text and mapped to the mean opinion scores (MOS) using the regression function. The second contribution of this work is to estimate the probability of four common document image distortion types, namely, paper translucency, stain, readers annotations and worn holes in the degraded images. In our experiment, the correlations of seven no-reference image quality assessment (NR-IQA) metrics with the MOS values are evaluated on two available datasets. It is shown that the performance of MDQM metric is significantly better than the state-of-the-art NR-IQA metrics. Moreover, the experimental results demonstrate that MDQM metric not only leads to high efficacy for classification of the various degradations but also maintains a remarkable run-time efficiency. It is worth to mention that the proposed method has been conducted for Arabic documents.

Blind quality assessment of gamut-mapped images via local and global statistical analysis

Gamut mapping is a key technology to achieve high-quality cross-media color reproduction. To optimize a gamut mapping algorithm, an important step is to conduct an accurate evaluation of its psycho-visual performance. This paper presents an objective blind image quality assessment (BIQA) metric for gamut-mapped images based on natural scene statistics. Considering both the local and global aspects of distortions in gamut-mapped images, two categories of statistics are analyzed. Specifically, the local statistical features are used to portray structural and color distortions and features extracted from global statistics are utilized to characterize the naturalness of image. The proposed metric does not need ground truth quality scores for training, thus it is ”completely” blind. Experimental results on three gamut mapping databases demonstrate that our method outperforms the state-of-the-art general-purpose BIQA models. To further validate its effectiveness, the proposed metric is applied for benchmarking GMAs as an application and achieves encouraging performance.

A new dual subband fast NLMS adaptive filtering algorithm for blind speech quality enhancement and acoustic noise reduction

This paper discusses the problem of acoustic noise reduction and speech enhancement through the forward blind source separation structure. Recently we have proposed a new combination between the forward blind source separation structure and the fast normalized least mean square algorithm that provides an efficient dual algorithm for noise reduction and speech enhancement applications. In this paper we propose a new subband implementation of this recent dual algorithm, this last allows improving the speed convergence behavior of the previous proposed algorithm in its fullband form. The performance of the proposed dual subband algorithm is compared with its fullband version of the dual fast normalized least mean square algorithm and the classical fullband dual normalized least mean square algorithm, and the two channel subband forward algorithm in terms of several objective criteria. The obtained results show the good performances of the proposed dual subband algorithm.

Learning content-specific codebooks for blind quality assessment of screen content images

This paper presents a novel blind quality assessment method for screen content images (SCIs) by learning content-specific codebooks. Instead of manually extracting quality-aware features for quality evaluation, the proposed method automatically generates effective features based on a simple feature encoding technique over content-specific codebooks. Considering the mixed content type in SCIs, content-specific codebooks including textual codebook and pictorial codebook are first learned in an offline manner. Given an input SCI, a textual/pictorial segmentation method is first applied to divide the SCI into textual and pictorial regions. Then, patches in different regions are respectively encoded using the learned textual and pictorial codebooks to produce the corresponding feature codes. Finally, the feature codes of each patch are aggregated, by using a percentage-based local pooling scheme, to yield the global feature codes of different regions. The final quality-predictive features used for quality regression are the combined global feature codes of different regions. Despite its simplicity, our method delivers low computational complexity, making it well suitable for real-time applications. Extensive experiments are conducted on three public SCI databases to validate the performance of our method, the results well confirm its superiority over the existing relevant full reference and no reference SCI quality assessment methods.

Deep blind quality evaluator for multiply distorted images based on monogenic binary coding

Perceptual quality evaluation of multiply distorted images has become a very challenging research topic. In this paper, we present a novel and efficient deep blind quality evaluator for multiply distorted images based on monogenic binary coding (MBC). Local complementary structural information and a deep learning method are employed to blindly evaluate the quality of multiply distorted images. First, a monogenic signal representation is utilized to decompose a multiply distorted image into three complementary components: orientation, phase, and magnitude. The quality-predictive features are then determined from the complementary components. Finally, the features are mapped to the human quality score of the multiply distorted image based on the deep neural network. The results on two newly established multiply distorted image subjective databases confirm that our metric has a better prediction performance than existing state-of-the-art full-reference and classical blind metrics.

BLIQUE-TMI: Blind Quality Evaluator for Tone-Mapped Images Based on Local and Global Feature Analyses

High dynamic range (HDR) image, which has a powerful capacity to represent the wide dynamic range of real-world scenes, has been receiving attention from both academic and industrial communities. Although HDR imaging devices have become prevalent, the display devices for HDR images are still limited. To facilitate the visualization of HDR images in standard low dynamic range displays, many different tone mapping operators (TMOs) have been developed. To create a fair comparison of different TMOs, this paper proposes a BLInd QUality Evaluator to blindly predict the quality of Tone-Mapped Images (BLIQUE-TMI) without accessing the corresponding HDR versions. BLIQUE-TMI measures the quality of TMIs by considering the following aspects: 1) visual information; 2) local structure; and 3) naturalness. To be specific, quality-aware features related to the former two aspects are extracted in a local manner based on sparse representation, while quality-aware features related to the third aspect are derived based on global statistics modeling in both intensity and color domains. All the extracted local and global quality-aware features constitute a final feature vector. An emergent machine learning technique, i.e., extreme learning machine, is adopted to learn a quality predictor from feature space to quality space. The superiority of BLIQUE-TMI to several leading blind IQA metrics is well demonstrated on two benchmark databases.

Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images.

Noise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.