Image quality measure via a quadtree homogeneity analysis

There are many uses of an image quality measure. It is often used to evaluate the effectiveness of an image processing algorithm, yet there is no one widely used objective measure. It can be used to compare similarity between two-dimensional data. In many papers, the mean squared error (MSE) or peak signal to noise ratio (PSNR) are used. These measures rely on pixel intensities instead of image structure. Though these measures are well understood and easy to implement, they do not correlate well with perceived image quality. This paper will present an image quality metric that analyzes image structure rather than entirely on pixels. It extracts image structure with the use of quadtree decomposition. A similarity comparison function based on contrast, luminance, and structure will be presented.

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

Comparing two similar images is often needed to evaluate the effectiveness of an image processing algorithm. But, there is no one widely used objective measure. In many papers, the mean squared error (MSE) or peak signal to noise ratio (PSNR) are used. These measures rely entirely on pixel intensities. Though these measures are well understood and easy to implement, they do not correlate well with perceived image quality. This paper will present an image quality metric that analyzes image structure rather than entirely on pixels. It extracts image structure with the use of a recursive quadtree decomposition. A similarity comparison function based on contrast, luminance, and structure will be presented.

Comparing two similar images is often needed to evaluate the effectiveness of an image processing algorithm. But, there is no one widely used objective measure. In many papers, the mean squared error (MSE) or peak signal to noise ratio (PSNR) are used. These measures rely entirely on pixel intensities. Though these measures are well understood and easy to implement, they do not correlate well with perceived image quality. This paper will present an image quality metric that analyzes image structure rather than entirely on pixels. It extracts image structure with the use of a recursive quadtree decomposition. A similarity comparison function based on contrast, luminance, and structure will be presented.

Image quality metrics have been widely used in imaging systems to maintain and improve the quality of images being processed and transmitted. Due to the close relationship between image quality perception and the human visual system, the development of image quality metrics has been contributed to by both psychologists and computer scientists. In this paper, three novel image quality metrics are proposed by improving the well-known image quality metric structural similarity index (SSIM). In this new approach, images are not compared directly, but their feature maps are (preprocessing is incorporated to extract the corner, edge and symmetry maps). The similarity measured (by SSIM) between corner, edge and symmetry maps of images being compared is used as an indicator of image quality, and named C SSIM, E SSIM and S SSIM respectively. The experiments show that all the proposed image quality metrics have a better performance than SSIM, and E SSIM has the best performance among them.

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

Image quality metrics have become more and more popular in the image processing community. However, so far, no one has been able to define an image quality metric well correlated with the percept for overall image quality. One of the causes is that image quality is multi-dimensional and complex. One approach to bridge the gap between perceived and calculated image quality is to reduce the complexity of image quality, by breaking the overall quality into a set of quality attributes. In our research we have presented a set of quality attributes built on existing attributes from the literature. The six proposed quality attributes are: sharpness, color, lightness, artifacts, contrast, and physical. This set keeps the dimensionality to a minimum. An experiment validated the quality attributes as suitable for image quality evaluation. The process of applying image quality metrics to printed images is not straightforward, because image quality metrics require a digital input. A framework has been developed for this process, which includes scanning the print to get a digital copy, image registration, and the application of image quality metrics. With quality attributes for the evaluation of image quality and a framework for applying image quality metrics, a selection of suitable image quality metrics for the different quality attributes has been carried out. Each of the quality attributes has been investigated, and an experimental analysis carried out to find the most suitable image quality metrics for the given quality attributes. For the sharpness attributes the Structural SIMilarity index (SSIM) by Wang et al. (2004) is the the most suitable, and for the other attributes further evaluation is required.

Image quality assessment is still an active field of research. The main objective of the developed image quality metric is to offer an index of quality that is consistent with the human subjective judgment of image quality. Despite the great number of developed metrics, there is still a need for image analysis tools that is able to extract the most perceptual relevant characteristics of an image. The goal of this work is then to propose a more advanced analysis and representation tools to extract more effective features that could be incorporated in the design of the image quality metric. In this paper, we propose a novel objective metric based on wave atoms transform. This new transform is half multiscale and half multi-directional. It offers a better representation of images containing oscillatory patterns and textures than the others known transforms. In this work, we propose a new full reference image quality metric based on wave atom transform and exploiting some properties of the human visual system. The consistency of the proposed metric with subjective evaluation is performed on LIVE database. The obtained correlation of this metric with the MOS provided by the database is better than other known metrics confirming thus the efficiency of this new image quality measure in predicting image quality.

Photoacoustic computed tomography (PACT) combines the advantages of optical and ultrasound imaging, offering significant potential for both preclinical and clinical applications. The development and refinement of PACT imaging systems and reconstruction methods should ideally be guided by performance measures that reflect the clinical utility of the images produced. However, widely employed conventional image quality (IQ) measures, including mean squared error (MSE) and structural similarity index (SSIM), do not always correlate with the utility of images with consideration of a specified diagnostic task, such as lesion detection. To address this, task-based IQ measures can be employed, which directly quantify the utility of an image for a specified clinical purpose. However, currently, such task-based IQ measures are not widely utilized in the photoacoustics research community. In this study, a task-based IQ measure is introduced and employed to quantify the performance of PACT reconstruction methods with respect to a clinically significant task, namely binary signal detection. Through systematic virtual imaging studies that involve realistic numerical breast phantoms and both physicsand learning-based image reconstruction methods, the advantages of task-based over conventional IQ measures are demonstrated. Examples are provided in which conventional IQ measures suggest comparable performance across reconstruction methods, while the task-based IQ measure demonstrates substantial differences, particularly when clinically significant lesions are missed. These results highlight the limitations of conventional IQ metrics in characterizing the diagnostic performance of PACT methods and underscore the necessity of integrating task-based IQ assessment into the development and evaluation of PACT reconstruction methods for clinical translation.

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User-oriented image quality assessment has become a key factor in multimedia communications. However, existing image quality metrics such as peak signal-to-noise ratio (PSNR) are inappropriate for in-service quality monitoring, since they require the original image to be available at the receiver. Although PSNR and others are objective metrics, they are not based on human visual perception. In order to overcome these problems, we propose a novel reduced-reference objective hybrid image quality metric (HIQM) that accounts for human visual perception and does not require a reference image at the receiver. This approach is based on a combination of various image artifact measures. The result is a single number, which represents an overall image quality. Experimental results indicate that HIQM outperforms PSNR.

Motion-induced artifact detection has become a fixture in the assessment of functional magnetic resonance imaging (fMRI) quality control. However, the effects of other MR image quality (IQ) metrics on intrinsic connectivity brain networks are largely unexplored. Accordingly, we report herein the initial assessment of the effects of a comprehensive list of IQ metrics on resting state networks using a multivariate analysis of covariance (MANCOVA) approach based on high-order spatial independent component analysis (ICA). Three categories of MR IQ metrics were considered: (1) metrics for artifacts including the AFNI outlier ratio and quality index, framewise displacement, and ghost to signal ratio, (2) metrics for the temporal quality of MRI data including the temporal framewise change in global BOLD signals (DVARS), global correlation of time-series, and temporal signal to noise ratio, (3) metrics for the structural quality of MRI data including the entropy focus criterion, foreground-background energy ratio, full-width half maximum smoothness, and static signal to noise ratio. After FDR-correction for multiple comparisons, results showed significant effects of the static and temporal signal to noise ratios on the spatial map intensities of the basal ganglia, default-mode and cerebellar networks. AFNI outlier ratio, framewise displacement and DVARS exhibited significant effects on the BOLD power spectra of sensorimotor networks. The global correlation of time-series displayed wide-spread modulation of the spectral power in most networks. Further investigations of the effect of IQ metrics on the characteristics of intrinsic connectivity brain networks allow more accurate interpretation of the fMRI results.

The detective quantum efficiency (DQE) and the effective DQE (eDQE) are relevant metrics of image quality for digital radiography detectors and systems, respectively. The current study further extends the eDQE methodology to technique optimization using a new metric of the effective dose efficiency (eDE), reflecting both the image quality as well as the effective dose (ED) attributes of the imaging system. Using phantoms representing pediatric, adult and large adult body habitus, image quality measurements were made at 80, 100, 120 and 140 kVp using the standard eDQE protocol and exposures. ED was computed using Monte Carlo methods. The eDE was then computed as a ratio of image quality to ED for each of the phantom/spectral conditions. The eDQE and eDE results showed the same trends across tube potential with 80 kVp yielding the highest values and 120 kVp yielding the lowest. The eDE results for the pediatric phantom were markedly lower than the results for the adult phantom at spatial frequencies lower than 1.2–1.7 mm−1, primarily due to a correspondingly higher value of ED per entrance exposure. The relative performance for the adult and large adult phantoms was generally comparable but affected by kVps. The eDE results for the large adult configuration were lower than the eDE results for the adult phantom, across all spatial frequencies (120 and 140 kVp) and at spatial frequencies greater than 1.0 mm−1 (80 and 100 kVp). Demonstrated for chest radiography, the eDE shows promise as an application-specific metric of imaging performance, reflective of body habitus and radiographic technique, with utility for radiography protocol assessment and optimization.

This paper proposed to extend the ATD steganographic method to work with color images and conduct an experimental study of the modified algorithm. In the original ATD algorithm, which works on grayscale images, The main idea is embedding the secret message as the ternary number and in each pixel of cover image is embedding two ternary numbers. For color images. we propose embedding four ternary numbers within each pixel of the cover image. As is well known, color images are composed of three primary color channels: red, green, and blue. The final color is generated by blending specific intensities of these three channels. Accordingly, three embedding combinations (112,121,211) were proposed to determine how the four ternary numbers are distributed among the red, green, and blue channels of each pixel. Each such combination corresponds to approximately 8 BPP for example, using the combination 211 means embedding two ternary numbers in the red channel (R), and one ternary number in each of the green (G) and blue (B) channels. By using the Matlab program, we compared the results on eight color images selected as cover images to evaluate the performance of the proposed algorithm. Image quality metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE), were used to evaluate the algorithm’s performance according to our analysis, the value of PSNR is not affected by the scheme but depends on image; PSNR is in range from 34.50 dB to 37.57 dB in same embedding capacity 8 BPP. Moreover, SNR values across the different embedding combinations, suggesting comparable performance among them. Keywords: Steganography, ATD, Peak Signal to Noise Ratio (PSNR), Embedding Capacity, Bit Per Pixel (BPP).