General-purpose Image Quality Assessment Research Articles

Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images.

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images

<p>Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.</p>

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

Adaptive optics-corrected solar image quality assessment based on image power spectrum and human visual system

In ground-based large aperture solar telescopes, the speckle image reconstruction technique combined with adaptive optics (AO) correction is generally used to get near diffraction-limited solar images. In order to select the high quality images from the AO-corrected high resolution solar image sequence, an automated no-reference image quality assessment (IQA) is needed. According to the noise characteristics of solar AO images, an IQA metric based on an image power spectrum and human visual system is developed. By the incorporation of noise masking and shifting the spatial frequency range of summation, our IQA metric could select sharper images with less noise than previous works based on the image power spectrum, even if there is image scaling. Compared with existing general-purpose IQA metrics and previous metrics specifically designed for solar IQA, experimental results verify that the proposed metric gains better performance whether on robustness to blur and noise, or on selecting high quality frames containing solar granulations, sunspots, or both of them. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

NO-REFERENCE IMAGE QUALITY ASSESSMENT FOR ZY3 IMAGERY IN URBAN AREAS USING STATISTICAL MODEL

Abstract. More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.

NO-REFERENCE IMAGE QUALITY ASSESSMENT FOR ZY3 IMAGERY IN URBAN AREAS USING STATISTICAL MODEL

More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.

General-purpose image quality assessment based on distortion-aware decision fusion

General-purpose image quality metrics aiming for quality prediction across various distortion types exhibit, on the whole, very limited effectiveness. In this paper, we propose a two-stage scheme to alleviate this limitation. At the first stage, probabilistic knowledge about the image distortion types is obtained based on a support-vector classification method. At the second stage, decision fusion of three existing image quality metrics is performed using the k-nearest-neighbor (k-NN) regression where the aforementioned probabilistic knowledge is utilized under an adaptive weighting scheme. We evaluate our method on the TID2008 database that is the largest publicly available image quality database containing 17 distortion types. The results strongly support the effectiveness and robustness of our method.

Color Distribution Information for the Reduced-Reference Assessment of Perceived Image Quality

Reduced-reference systems can predict in real-time the perceived quality of images for digital broadcasting, only requiring that a limited set of features, extracted from the original undistorted signals, is transmitted together with the image data. This paper uses descriptors based on the color correlogram, analyzing the alterations in the color distribution of an image as a consequence of the occurrence of distortions, for the reduced reference data. The processing architecture relies on a double layer at the receiver end. The first layer identifies the kind of distortion that may affect the received signal. The second layer deploys a dedicated prediction module for each type of distortion; every predictor yields an objective quality score, thus completing the estimation process. Computational-intelligence models are used extensively to support both layers with empirical training. The double-layer architecture implements a general purpose image quality assessment system, not being tied up to specific distortions and, at the same time, it allows us to benefit from the accuracy of specific, distortion-targeted metrics. Experimental results based on subjective quality data confirm the general validity of the approach.