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.

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.

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.

Sparse Representations-based depth images quality assessment

The conventional 2D metrics can be used for measuring the quality of depth maps,but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality.In this paper,we propose a new full reference objective metric,called Sparse Representations-Mean Squared Error(SR-MSE),which efficiently evaluates the depth maps compression distortions.It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features.Then,it computes the mean squared error between the sparse coefficients issued from this modeling.As a benchmark of quality assessment,we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates.We compare the subjective results with the proposed and conventional objective metrics.Experimental results demonstrate that the proposed SR-MSE,compared to the conventional image quality assessment metrics,yields the highest correlated scores to the subjective ones.

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.

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.

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%.

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.

Reproducibility study of intravoxel incoherent motion and apparent diffusion coefficient parameters in normal pancreas

BACKGROUND The consistency of pancreatic apparent diffusion coefficient(ADC)values and intravoxel incoherent motion(IVIM)parameter values across different magnetic resonance imaging(MRI)devices significantly impacts the patient’s diagnosis and treatment.AIM To explore consistency in image quality,ADC values,and IVIM parameter values among different MRI devices in pancreatic examinations.METHODS This retrospective study was approved by the local ethics committee,and informed consent was obtained from all participants.In total,22 healthy volunteers(10 males and 12 females)aged 24-61 years(mean,28.9±2.3 years)underwent pancreatic diffusion-weighted imaging using 3.0T MRI equipment from three vendors.Two independent observers subjectively scored image quality and measured the pancreas’s overall ADC values and signal-to-noise ratios(SNRs).Subsequently,regions of interest(ROIs)were delineated for the IVIM parameters(true diffusion coefficient,pseudo-diffusion coefficient,and perfusion fraction)using post-processing software.These ROIs were on the head,body,and tail of the pancrease.The subjective image ratings were assessed using the kappa consistency test.Intraclass correlation coefficients(ICCs)and mixed linear models were used to evaluate each device’s quantitative parameter values.Finally,a pairwise analysis of IVIM parameter values across each device was performed using Bland-Altman plots.RESULTS The Kappa value for the subjective ratings of the different observers was 0.776(P<0.05).The ICC values for interobserver and intra-observer agreements for the quantitative parameters were 0.803[95% confidence interval(CI):0.684-0.880]and 0.883(95%CI:0.760-0.945),respectively(P<0.05).The ICCs for the SNR between different devices was comparable(P>0.05),and the ICCs for the ADC values from different devices were 0.870,0.707,and 0.808,respectively(P<0.05).Notably,only a few statistically significant inter-device agreements were observed for different IVIM parameters,and among those,the ICC values were generally low.The mixed linear model results indicated differences(P<0.05)in the f-value for the pancreas head,D-value for the pancreas body,and D-value for the pancreas tail obtained using different MRI machines.The Bland-Altman plots showed significant variability at some data points.CONCLUSION ADC values are consistent among different devices,but the IVIM parameters’repeatability is moderate.Therefore,the variability in the IVIM parameter values may be associated with using different MRI machines.Thus,caution should be exercised when using IVIM parameter values to assess the pancreas.

Effect of erythromycin on image quality and transit time of capsule endoscopy: A two-center study

AIM: To compare the effect of oral erythromycin vs no preparation with prokinetics on the transit time and the image quality of capsule endoscopy (CE) in evaluating small bowel (SB) pathology. METHODS: We conducted a retrospective, blinded (to the type of preparation) review of 100 CE studies, 50 with no preparation with prokinetics from one medical center (Group A) and 50 from another center with administration of a single dose of 200 mg oral erythromycin 1 h prior to CE (Group B). Gastric, SB and total transit times were calculated, the presence of bile in the duodenum was scored, as was cleanliness within the proximal, middle and distal intestine. RESULTS: The erythromycin group had a slightly shorter gastric transit time (21 min vs 28 min, with no statistical significance). SB transit time was similar for both groups (all P > 0.05). Total transit time was almost identical in both groups. The rate of incomplete examination was 16% for Group A and 10% for Group B (P = 0.37). Bile and cleanliness scores in different parts of the intestine were similar for the two groups (P > 0.05). CONCLUSION: Preparation for capsule endoscopy with erythromycin does not affect SB or total transit time. It tends to reduce gastric transit time, but it does not increase the cecum-reaching rate. Erythromycin does not adversely affect image quality. We consider the routine use of oral erythromycin preparation as being unjustified, although it might be considered in patients with known prolonged gastric emptying time.

Effects of Saline Administration, Abdominal Compression, and Prolongation of Acquisition Delay on Image Quality Improvement of CT Urography

Objective To retrospectively evaluate the effects of saline administration following contrast material injection, abdominal compression and two delay phase acquisition on image quality improvement of computed tomographic urography (CTU). Methods Medical records and informed consents of patients were obtained. In totally 122 patients (50 men, 72 women), two delay phase images with CTU were performed. Scans began simultaneously with a contrast bolus injection of 100 mL (300 mgI/mL) followed by a saline bolus injection of 100 mL at a rate of 5 mL/s. Two delay phase images were taken at 400 and 550 seconds for each patient. Examinations were taken by using abdominal compression or not. The distention and opacification of the urinary tract were evaluated by two interpreters together on transverse images and post-processing images. Effects of four techniques (saline administration and abdominal compression, saline administration only, compression only, and neither saline administration nor compression) and two delay phase acquisition on image quality improvement were analysed by using ANOVA and Chi-square test. Results Saline administration improved opacification (P<0.05) and increased overall image quality (P<0.01) of the intrarenal collecting system and proximal ureter. Abdominal compression (P<0.05) and delayed phase image acquisition of 550 seconds (P<0.01) all improved distention of the intrarenal collecting system and proximal ureter but did not improve opacification. No statistically significant effects on the distal ureter were found. However, there were more visualized distal ureteral segments with the longer imaging delay. Conclusion Saline administration, abdominal compression and longer imaging delays are all effective in improving image quality of 64-detector row CTU.

Multi-Modality Medical Image Fusion Based on Wavelet Analysis and Quality Evaluation

Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.

Image Tampering Detection Using No-Reference Image Quality Metrics

In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

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.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

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.

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.

Using Sobel Operator for Automatic Edge Detection in Medical Images

The problems of installation and integration of complex suite of software for processing medical images. Based analysis of the situation is realized in an easier integration of an automated system using the latest information technologies using the web - environment for analysis and segmentation of DICOM - images.

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.