Bridge the Gap Between Full-Reference and No-Reference:A Totally Full-Reference Induced Blind Image Quality Assessment via Deep Neural Networks

Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.

Blind Image Quality Assessment by Pairwise Ranking Image Series

Image quality assessment(IQA)is constantly innovating,but there are still three types of stickers that have not been resolved:the“content sticker”-limitation of training set,the“annotation sticker”-subjective instability in opinion scores and the“distortion sticker”-disordered distortion settings.In this paper,a No-Reference Image Quality Assessment(NR IQA)approach is proposed to deal with the problems.For“content sticker”,we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network;For“annotation sticker”,the absolute noise-containing subjective scores are transformed into ranking comparison results,and we design an indirect unsupervised regression based on EigenValue Decomposition(EVD);For“distortion sticker”,we propose a perception-based distortion classification method,which makes the distortion types clear and refined.Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability.Furthermore,the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

Image quality assessment based on perceptual grouping

To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.

Color Image Quality Assessment Based on Structural Similarity

It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.

SIMPLE QUALITY ASSESSMENT FOR BINARY IMAGES

Usually image assessment methods could be classified into two categories: subjective as-sessments and objective ones. The latter are judged by the correlation coefficient with subjective quality measurement MOS (Mean Opinion Score). This paper presents an objective quality assessment algorithm special for binary images. In the algorithm, noise energy is measured by Euclidean distance between noises and signals and the structural effects caused by noise are described by Euler number change. The assessment on image quality is calculated quantitatively in terms of PSNR (Peak Signal to Noise Ratio). Our experiments show that the results of the algorithm are highly correlative with subjective MOS and the algorithm is more simple and computational saving than traditional objective assessment methods.

NEW VISUAL PERCEPTUAL POOLING STRATEGY FOR IMAGE QUALITY ASSESSMENT

Most of Image Quality Assessment (IQA) metrics consist of two processes. In the first process, quality map of image is measured locally. In the second process, the last quality score is converted from the quality map by using the pooling strategy. The first process had been made effective and significant progresses, while the second process was always done in simple ways. In the second process of the pooling strategy, the optimal perceptual pooling weights should be determined and computed according to Human Visual System (HVS). Thus, a reliable spatial pooling mathematical model based on HVS is an important issue worthy of study. In this paper, a new Visual Perceptual Pooling Strategy (VPPS) for IQA is presented based on contrast sensitivity and luminance sensitivity of HVS. Experimental results with the LIVE database show that the visual perceptual weights, obtained by the proposed pooling strategy, can effectively and significantly improve the performances of the IQA metrics with Mean Structural SIMilarity (MSSIM) or Phase Quantization Code (PQC). It is confirmed that the proposed VPPS demonstrates promising results for improving the performances of existing IQA metrics.

A METHOD OF IMAGE QUALITY ASSESSMENT FOR COMPRESSIVE SAMPLING VIDEO TRANSMISSION

Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension. By this method, the auxiliary information of the recovery image quality is obtained as a feedback to control number of measurements from compressive sampling video stream. Therefore, the number of measurements can be easily derived at the condition of the absence of information sparsity, and the recovery image quality is effectively improved. Theoretical and experimental results show that this algorithm can estimate the quality of images effectively and is in well consistency with the traditional objective evaluation algorithm.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

Blind Image Quality Assessment Based on Hybrid Fuzzy-Genetic Technique

A new method for no-reference image quality assessment based on hybrid fuzzy-genetic technique is proposed. Noise variance and edge sharpness level of the restored image are two basic metrics for assessing the performance of the restoration algorithm, then a fuzzy if-then inference system is developed to combine the two metrics to get a final quality score, and the parameters of the fuzzy membership function are trained with genetic algorithms. Experiments results show that the image quality score correlates well with mean opinion score and the proposed approach is robust and effective.

Blind Image Quality Assessment Based on Wavelet Power Spectrum in Perceptual Domain

Blind image quality assessment(BIQA) can assess the perceptual quality of a distorted image without a prior knowledge of its reference image or distortion type. In this paper, a novel BIQA model is developed in wavelet domain. Considering the multi-resolution and band-passing characteristics of discrete wavelet transform(DWT), an improvement over the power spectrum is put forward, i.e., dubbed wavelet power spectrum(WPS)estimation. Then, the concept of directional WPS is applied to simplify the calculation. Moreover, a rotationally symmetric modulation transfer function(MTF) of human visual system(HVS) is integrated as a filter, which makes the metric to be consistent with the human vision perception and more discriminative. Experiments are conducted on the LIVE databases and three other databases, and the results show that the proposed metric is highly correlated with subjective evaluations and it competes well with other state-of-the-art metrics in terms of effectiveness and robustness.

No-Reference Stereo Image Quality Assessment Based on Transfer Learning

In order to apply the deep learning to the stereo image quality evaluation,two problems need to be solved:The first one is that we have a bit of training samples,another is how to input the dimensional image’s left view or right view.In this paper,we transfer the 2D image quality evaluation model to the stereo image quality evaluation,and this method solves the first problem;use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem.At the same time,the input image is preprocessed by phase congruency transformation,which further improves the performance of the algorithm.The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers.The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.

Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution

Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer vision tasks,many IQA methods start to utilize the deep convolutional neural networks(CNN)for IQA task.In this paper,a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution,which consists of two tasks:A distortion recognition task and a quality regression task.For the first task,image distortion type is obtained by the fully connected layer.For the second task,the image quality score is predicted during the distortion recognition progress.Experimental results on three famous IQA datasets show that the proposed method has better performance than the previous traditional algorithms for quality prediction and distortion recognition.

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

Toward a Neurophysiological Measure of Image Quality Perception Based on Algebraic Topology Analysis

The bandwidth of internet connections is still a bottleneck when transmitting large amounts of images,making the image quality assessment essential.Neurophysiological assessment of image quality has highlight advantages for it does not interfere with natural viewing behavior.However,in JPEG compression,the previous study is hard to tell the difference between the electroencephalogram(EEG)evoked by different quality images.In this paper,we propose an EEG analysis approach based on algebraic topology analysis,and the result shows that the difference between Euler characteristics of EEG evoked by different distortion images is striking both in the alpha and beta band.Moreover,we further discuss the relationship between the images and the EEG signals,and the results implied that the algebraic topological properties of images are consistent with that of brain perception,which is possible to give birth to braininspired image compression based on algebraic topological features.In general,an algebraic topologybased approach was proposed in this paper to analyze the perceptual characteristics of image quality,which will be beneficial to provide a reliable score for data compression in the network and improve the network transmission capacity.

Research on assessing compression quality taking into account the space-borne remote sensing images

According to the remote sensing image characteristics, a set oi optimized compression quahty assessment methods is proposed on the basis of generating simulative images. Firstly, a means is put forward that generates simulative images by scanning aerial films taking into account the space-borne remote sensing camera characteristics （including pixel resolution, histogram dynamic range and quantization）. In the course of compression quality assessment, the objective assessment considers images texture changes and mutual relationship between simulative images and decompressed ima- ges, while the synthesized estimation factor （SEF） is brought out innovatively for the first time. Subjective assessment adopts a display setup -- 0.5mrn/pixel, which considers human visual char- acteristic and mainstream monitor. The set of methods are applied in compression plan design of panchromatic camera loaded on ZY-1-02C satellite. Through systematic and comprehensive assess- ment, simulation results show that image compression quality with the compression ratio of d：l can meet the remote sensing application requirements.

Semi-reference image quality metric for virtual view images in free-viewpoint television

A semi-reference image quality assessment metric based on similarity measurement for synthesized virtual viewpoint image （VVI） in free-viewpoint television system （FFV） is proposed in this paper. The key point of the proposed metric is taking resemblant information between VVI and its neighbor view images for quality assessment to make our metric to be extended to multi-semi-reference image quality assessment easily. The proposed metric first extracts impact factors from image features, then combines an image synthesis technique and similarity functions, in which, disparity information are taken into account for registering the resemblant regions. Experiments are divided into three phases. Phase I is to verify the validation of the proposed metric by taking impaired images and original reference into account. The experimental results show the agreement between evaluation scores and bio-characteristic of human visual system. Phase II shows the accordance with Phase I by taking neighbor view as reference. The proposed metric can be taken as a full reference one to evaluate the image quality even though the original reference is absent. Phase III is then performed to evaluate the quality of WI. Evaluation scores in the experimental results are able to evaluate the quality of VVI.

A NEW OBJECTIVE STEREOSCOPIC IMAGE ASSESSMENT MODEL BASED ON STEREOSCOPIC PERCEPTION

In order to establish a stereoscopic image quality assessment method which is consistent with human visual perception,we propose an objective stereoscopic image quality assessment method.It takes into account the strong correlation and high degree of structural between pixels of image.This method contains two models.One is the quality synthetic assessment of left-right view images,which is based on human visual characteristics,we use the Singular Value Decomposition(SVD)that can represent the degree of the distortion,and combine the qualities of left and right images by the characteristics of binocular superposition.The other model is stereoscopic perception quality assessment,due to strong stability of image’s singular value characteristics,we calculate the distance of the singular values and structural characteristic similarity of the absolute difference maps,and utilize the statistical value of the global error to evaluate stereoscopic perception.Finally,we combine two models to describe the stereoscopic image quality.Experimental results show that the correlation coefficients of the proposed assessment method and the human subjective perception are above 0.93,and the mean square errors are all less than 6.2,under JPEG,JP2K compression,Gaussian blurring,Gaussian white noise,H.264 coding distortion,and hybrid cross distortion.It indicates that the proposed stereoscopic objective method is consistent with human visual properties and also of availability.

Perceptual quality assessment of panoramic stitched contents for immersive applications:a prospective survey

The recent advancements in the field of Virtual Reality(VR)and Augmented Reality(AR)have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology(Soft Tech).VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360°imagery that widely used in the education,gaming,entertainment,and production sector.The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360°images,in fact a minor visual distortion can significantly degrade the overall quality.Thus,to ensure the quality of constructed panoramic contents for VR and AR applications,numerous Stitched Image Quality Assessment(SIQA)methods have been proposed to assess the quality of panoramic contents before using in VR and AR.In this survey,we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date.For better understanding,the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches.Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task.Further,we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents.In last,we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.

A multimodal dense convolution network for blind image quality assessment

Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

High Resolution SAR Image Algorithm with Sample Length Constraints for the Range Direction

The traditional Range Doppler(RD)algorithm is unable to meet practical needs owing to the limit of resolution.The order of fractional Fourier Transform(FrFT)and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm.To overcome the above shortcomings,the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction.The expression of the optimal order of SAR range signals via FrFT is deduced in detail.The initial sample length and its constraints are proposed to obtain the best sample length of SAR range signals.Experimental results demonstrate that,when the range sampling-length changes in a certain interval,the best sampling-length will be obtained,which the best values of the range resolution,PSLR and ISLR,will be derived respectively.Compared with traditional RD algorithm,the main-lobe width of the peak-point target of the proposed algorithm is narrow in the range direction.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.