
Culture can play a significant role in evaluating image quality. Therefore, this work considered one of the least studied cultural regions of observers, examining the impact of Central Asian culture on image quality evaluation. More specifically, it investigated how they evaluate the quality of contrast-enhanced images. It was found that observer evaluations vary and can be divided into groups. These groups may have their individual preferences for the quality of contrast-enhanced images. Therefore, the personalization factor should be incorporated into the quality evaluation of (contrast-) enhanced images. Furthermore, the results were compared with another population and differences were found in the overall outcomes of the two observer groups. The variations observed could be due to cultural differences. In addition, this study introduced the Central Asian Contrast-Enhanced Image Quality Dataset (CACEIQD). A variety of image quality metrics, including deep learning techniques, were tested on the dataset. The results indicate that the dataset is challenging and highlight an area for metric improvement. This dataset can be helpful for future research in the field of enhanced image quality evaluation.

Traditional spatial frequency response (SFR) measurement, as defined by the ISO 12233 slanted-edge methodology, encounters significant measurement uncertainties and operational constraints when applied to wide-angle imaging systems. While recent updates to the standard incorporate polynomial edge-fitting to mitigate geometric warping, the underlying linear-edge model remains inherently limited by directional anisotropy and sampling phase instabilities at critical field angles. Furthermore, the rigid alignment requirements of slanted edges—often compromised by distortion-induced slope deviations—necessitate time-consuming mechanical orientation of the device under test (DUT) or the test target. This paper proposes a robust Circular-Edge SFR framework that synthesizes the broadband spectral coverage of the slanted-edge with the rotational invariance of the Siemens star. By employing a sub-pixel centroiding algorithm and a 60° tangent-aligned sector projection, the proposed method achieves continuous sampling phase integration and simultaneous extraction of SFR in any orientation, e.g., sagittal and tangential. Validation using synthetic equidistant projection models at a 100° field angle demonstrates that the circular-edge maintains high-fidelity measurements where traditional slanted edges collapse due to localized geometric stress. Notably, the superior azimuthal robustness and rotational symmetry of circles eliminate the 'critical angle' sampling failures. The framework provides a high-precision, alignment-independent solution for evaluating the image quality of wide-angle and fisheye camera systems.

In camera product development, where the goal is to achieve the best possible image quality and user experience, it is necessary to use both objective and subjective test methods. Both methods have their own advantages and disadvantages. The goal of this study is to bring these methods closer together and help the user of objective tests understand the meaning of the test result for the end user. Objective image quality measures are fast and efficient. They form the basis for, for example, camera product comparisons daily basis. The comparison of two camera products is completed quickly and the result is reliable and repeatable. Based on the results provided by the measure, it is possible to rank any camera products easily. However, how big is the noticeable difference between two products for a user if such an objective measure is used? When is the difference significant? Or does the user notice the measured difference at all? In this study, we wanted to get answers to these questions for the acutance measure which is used daily basis. We conducted numerous subjective tests in a controlled lab with carefully chosen stimuli. To include the effect of image content in the study, we used both an image with a lot of detail and a test image with a lot of flat areas and little detail as test samples. Based on these subjective results, we calculated the corresponding Just Noticeable Difference (JND) values for our acutance measure. Results were slightly different to image content with flat areas versus image content with a lot of detail. This study presents methods and results for finding JND values for an objective acutance measure that can be more broadly generalized to all objective acutance measures and, in terms of the method, to all objective measures.

In the past, high resolution was the trend in the mobile image sensor. Currently, smartphones equipped with 200 million pixel sensor. Recently, efforts have been made to increase profits through miniaturization of pixel size and reduction of chip size by increasing production. The smaller the pixel size, less light is received, with increases noise and deteriorates the image quality. Smartphone manufacturers request specialized features. Therefore, sensor development organization focuses on increasing profits and installing sensors with additional functions such as high sensitivity, high dynamic range, including autofocus pixel. Such efforts do not only bring good results in terms of image quality of the sensor. Inevitably, side effects occur. One of them is maze noise. In this paper, we introduce the image quality performance improved through digital processing of maze noise in CMOS mobile image sensor.

Vehicle-borne cameras vary greatly in imaging properties, e.g., angle of view, working distance and pixel count, to meet the diverse requirements of various applications. In addition, auto parts must tolerate dramatic variations in ambient temperature. These pose considerable challenges to the automotive industry when it comes to the evaluation of automotive cameras in terms of imaging performance. In this paper, an integrated and fully automated system, developed specifically to address these issues, is described. The key components include a collimator unit incorporating a LED light source and a transmissive test target, a mechanical structure that holds and moves the collimator and the camera under test, and a software suite that communicates with the controllers and computes the images captured by the camera. With the multifunctional system, imaging performance of cameras can be conveniently measured at a high degree of accuracy, precision and compatibility. The results are consistent with those obtained from tests conducted with conventional methods. Preliminary results demonstrate the potential of the system in terms of functionality and flexibility with continuing development.

We live in a visual world. The perceived quality of images is of crucial importance in industrial, medical, and entertainment application environments. Developments in camera sensors, image processing, 3D imaging, display technology, and digital printing are enabling new or enhanced possibilities for creating and conveying visual content that informs or entertains. Wireless networks and mobile devices expand the ways to share imagery and autonomous vehicles bring image processing into new aspects of society. The power of imaging rests directly on the visual quality of the images and the performance of the systems that produce them. As the images are generally intended to be viewed by humans, a deep understanding of human visual perception is key to the effective assessment of image quality.

Most cameras use a single-sensor arrangement with Color Filter Array (CFA). Color interpolation techniques performed during image demosaicing are normally the reason behind visual artifacts generated in a captured image. While the severity of the artifacts depends on the demosaicing methods used, the artifacts themselves are mainly zipper artifacts (block artifacts across the edges) and false-color distortions. In this study and to evaluate the performance of demosaicing methods, a subjective pair-comparison method with 15 observers was performed on six different methods (namely Nearest Neighbours, Bilinear interpolation, Laplacian, Adaptive Laplacian, Smooth hue transition, and Gradient-Based image interpolation) and nine different scenes. The subjective scores and scene images are then collected as a dataset and used to evaluate a set of no-reference image quality metrics. Assessment of the performance of these image quality metrics in terms of correlation with the subjective scores show that many of the evaluated no-reference metrics cannot predict perceived image quality.

Structure-aware halftoning algorithms aim at improving their non-structure-aware version by preserving high-frequency details, structures, and tones and by employing additional information from the input image content. The recently proposed achromatic structure-aware Iterative Method Controlling the Dot Placement (IMCDP) halftoning algorithm uses the angle of the dominant line in each pixel’s neighborhood as supplementary information to align halftone structures with the dominant orientation in each region and results in sharper halftones, gives a more three-dimensional impression, and improves the structural similarity and tone preservation. However, this method is developed only for monochrome halftoning, the degree of sharpness enhancement is constant for the entire image, and the algorithm is prohibitively expensive for large images. In this paper, we present a faster and more flexible approach for representing the image structure using a Gabor-based orientation extraction technique which improves the computational performance of the structure-aware IMCDP by an order of magnitude while improving the visual qualities. In addition, we extended the method to color halftoning and studied the impact of orientation information in different color channels on improving sharpness enhancement, preserving structural similarity, and decreasing color reproduction error. Furthermore, we propose a dynamic sharpness enhancement approach, which adaptively varies the local sharpness of the halftone image based on different textures across the image. Our contributions in the present work enable the algorithm to adaptively work on large images with multiple regions and different textures.

Structure-aware halftoning algorithms aim at improving their non-structure-aware version by preserving high-frequency details, structures, and tones and by employing additional information from the input image content. The recently proposed achromatic structure-aware Iterative Method Controlling the Dot Placement (IMCDP) halftoning algorithm uses the angle of the dominant line in each pixel’s neighborhood as supplementary information to align halftone structures with the dominant orientation in each region and results in sharper halftones, gives a more three-dimensional impression, and improves the structural similarity and tone preservation. However, this method is developed only for monochrome halftoning, the degree of sharpness enhancement is constant for the entire image, and the algorithm is prohibitively expensive for large images. In this paper, we present a faster and more flexible approach for representing the image structure using a Gabor-based orientation extraction technique which improves the computational performance of the structure-aware IMCDP by an order of magnitude while improving the visual qualities. In addition, we extended the method to color halftoning and studied the impact of orientation information in different color channels on improving sharpness enhancement, preserving structural similarity, and decreasing color reproduction error. Furthermore, we propose a dynamic sharpness enhancement approach, which adaptively varies the local sharpness of the halftone image based on different textures across the image. Our contributions in the present work enable the algorithm to adaptively work on large images with multiple regions and different textures.

In this paper, we study individual quality scores given by different observers for various image distortions (saturation, contrast, and color quantization) at different levels. We created a database that contains a total of 232 images, derived from 21 pristine images, three distortions, and five levels. The database was rated by 31 participants collected through an online platform. The study shows that observers have distinguishable patterns with respect to different distortions. Using quadratic regression models, we visualized the behavior patterns of different groups of observers. The database and the individual scores collected are publicly available and can be further used for quality assessment research.