CRA: A Real-Time CUDA-Based Resolution Adapter in Screen Recording Applications

Remote screen recording is a system that records whole actions on a remote computer screen, and displays the actions on a client computer screen at the same time. Traditionally, screen recording are streamed in a video format, which large data usually results in high CPU utilization and heavy network loading. Therefore, in order to reduce CPU utilization and network bandwidth loading, the screen recording system records and only sends the blocks which have updates on the recording screen. A screen update on any part of the recording screen is called a "changed block" in this paper. Only recording and sending the changed blocks definitely reduces the network bandwidth and CPU loading. Because of only sending changed blocks, this study comes up with a media synchronization problem between a video stream and remote screen data stream in a distance learning system due to an unstable network environment. This study proposes a fast skip mechanism, called Fast Media Synchronization (FMS), to match the video stream playback by adjusting the remote screen data stream. The FMS quickly determines whether the changed blocks overlap. In the fast skip mechanism of FMS, overlapping areas among changed blocks in a screen data stream will be effectively deleted. In this paper, four different scenarios web browsing, document editing, slide presentation, and video playback, are designed to test the effectiveness of applying FMS. The experiment results shows that the FMS mechanism approximately reduces 85% of processing time, 57% to 99% of the frequency of pasting times, 70% to 90% the number of comparisons, and 70% to 90% the total pasting area. Furthermore, the proposed FMS mechanism can also be applied to improve the efficiency of different screen recording systems, such as remote desktop, distance conferencing, and video streaming, etc.

Digital video playback on mobile devices is fast becoming widespread and popular. Since mobile devices are typically resource constrained in terms of network bandwidth, battery power and available screen resolution, it is often necessary to formulate special encoding techniques in order to optimize power consumption during video streaming and playback. The existing H.264 standard is popular for video encoding on mobile devices, since it results in a low-bitrate video with visual clarity that is adequate for video playback on mobile devices. However, due to the complexity of the H.264 representation, the video decoding procedure is typically computationally intensive. In this paper, we propose a novel lossy video representation termed as Ligne-Claire (LC) video. LC videos are obtained via graphics overlay of outlines or silhouettes of objects in the video over an approximated texture video. Since the playback of LC video is typically meant for mobile devices, the visual quality of video is adequate for most mobile applications wherein the semantic content of the video can be characterized by object shapes and approximate texture information. Experimental results presented in the paper demonstrate that the proposed lossy LC video encoding scheme results in power savings of 50% or more during video playback compared to standard H.264-encoded videos, of similar video file size. In order to evaluate the visual quality of the LC video, we compare the performance of LC videos with H.264-encoded videos in the context of some typical computer vision tasks. Our results indicate that the performance of the computer vision algorithms on these videos is similar. This fact, coupled with subjective evaluation, and the resulting significant power savings, indicates that the proposed LC representation can be used effectively to encode video for power-constrained mobile devices.

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.

In this paper, we propose two novel (non- parametric) statistical measures of image and video quality. Both of these are based on the fundamental notion of singular value decomposition, applied to local pixel and higher order information derived from the data. These methods enable measurement of similarity between images, and the measurement of image quality without a reference. A possible derivative application of the latter statistical measure is to optimize parameters of the relevant restoration algorithms being applied.

Combining multiple color image attribute measures by linear addition has been shown to be an effective method in evaluating color image and video quality. Therefore, appropriate selection of the linear combination coefficients determines the performance of the image quality measure. In this paper, training methods of obtaining the linear combination coefficients are discussed for two practical applications: (1) for image processing applications where images before and after processing have the same source; and (2) for video processing purpose where consecutive frames have different contents and suffer from different types of distortions. With the obtained linear combination coefficients, the overall image quality measure Color Quality Measure (CQM) is shown to be effective in benchmarking the qualities of images and video frames in terms that it has high correlations with subjective evaluation Mean Opinion Scores (MOS).

영상 크기 변환은 응용 환경에서의 다양한 목적을 위하여 사용되며, 변환 함수의 성능을 평가하기 위하여 처리전과 후의 두 영상의 화질을 비교한다. 변환 함수의 객관적 성능 평가를 위하여 화질 비교의 정확한 지표가 요구되며, 실제 다양한 관점에서의 접근이 이루어졌다. 하지만 영상 크기 변환 과정에서 생략되거나 새롭게 생성되는 화소들의 위치를 고려한 화질 비교 척도에 관한 연구는 거의 이루어지지 않고 있다. 따라서 본 논문에서는 영상 크기 변환에서 발생하는 화소들의 생략 및 추정 위치를 고려한 객관적 영상 화질 측정방법을 제안한다. 제안하는 방법은 기존의 영상 화질 척도를 이용하여 위치 변화를 고려한 새로운 영상화질 측정 방법을 만들고, 위치 변화에 대한 민감도를 평가하였다. 실험을 통해서 기존에 널리 사용되는 영상 화질 측정 방법이 화소들의 생략 및 추정 위치의 변화에 많은 영향을 받는 것을 알 수 있었고, 제안하는 방법이 화소들의 생략 및 추정 위치 변화를 고려한 영상 화질 표현의 객관적 척도임을 확인하였다. 제안하는 방법은 영상 복원 및 개선 함수의 성능을 평가하는 척도로서 유용하게 사용될 수 있다. Image scaling is used for a variety of real-life applications. In order to evaluate the performance of transform functions, the image quality are compared together before and after processing. For the objective evaluation of the transform functions, the exact criterion of image quality is required, and various aspects approaches are practically performed. However, few researches have been conducted on image quality measurement considering the position of pixels that are skipped or newly generated in the process of the image scaling. Therefore this paper focuses on the objective image quality measurement for positions of skipped or estimated pixels in the image scaling. The proposed method generated new image quality measure considering the positional changes using a conventional measure and evaluated sensitivity about positional changes. Through this experiments, it is observed that conventional image quality measurement is definitely affected by positional changes of a skipped and estimated pixels. It is also confirmed that the proposed method is an objective criterion to represent image quality for positional changes of skipped or estimated pixels. The proposed method can be used as a criterion to evaluate the performance of image restoration or enhancement functions.

Stereoscopic Image Retargeting (SIR) aims to adapt stereoscopic images and videos to 3D display devices with various aspect ratios by emphasizing the important content while retaining surrounding context with minimal visual distortion. To address the issue of SIR evaluation, this paper presents a new objective quality assessment method for retargeted stereopairs by combining image quality and depth perception measures. Specifically, the image quality measure is conducted between the source and retargeted intermediate views generated by the view synthesis method to characterize the geometric distortion and content loss of the retargeted stereopair, while several depth-aware features are extracted to measure the visual comfort/discomfort and depth sensation when human views a 3D scene. Then, the extracted features are integrated into an overall perceptual quality prediction. Experiment results on NBU SIRQA and SIRD databases verify the superiority of our method.

Image quality measurement is a major challenge in digital image processing field. All image quality methods compare two images by providing a quantitative score that describes the degree of similarity, or in other words, the level of distortion between them. In this work, we propose a new full reference image quality measure using some statistical functions called Copulas. To our knowledge, this is the first time that copulas are used for image quality measurement. Our algorithms use the steerable pyramid technique to decompose the original and the distorted images. Then we exploit some of copula functions properties to calculate the image quality of the distorted image (such as a forged image) with respect to its original. The experimental results of our method show that the effectiveness of our method is comparable or better than the state of the current state of the art methods. In addition, our method is simple and fast.

This paper deals with the quality of the image itself as well as the algorithm used for evaluating the quality of the fingerprints to construct an effective algorithm for evaluating the quality of the fingerprints. The quality of the fingerprint is acquired by taking the regional quality of each fingerprint image. The regional quality is acquired by measuring the quality of the fingerprint in blocks in the enrollment stage. The amount of fingerprint varies according to the size of each block which makes the result unsteady. We concentrated on finding the right size for the block used to acquire the fingerprint image as a graphical element. Also, the quality distribution included in the fingerprint image was acquired when the optimal block size was adopted. The threshold value of this distribution rate is expected to be used to acquire a high classification.Keywordsimage quality measureFingerprintsliced blockblock size optimisation

A computer program has been developed to model chest radiography. It incorporates a voxel phantom of an adult and includes antiscatter grid, radiographic screen, and film. Image quality is quantified by calculating the contrast (deltaOD) and the ideal observer signal-to-noise ratio (SNR(I)) for a number of relevant anatomical details at various positions in the anatomy. Detector noise and system unsharpness are modeled and their influence on image quality is considered. A measure of useful dynamic range is computed and defined as the fraction of the image that is reproduced at an optical density such that the film gradient exceeds a preset value. The effective dose is used as a measure of the radiation risk for the patient. A novel approach to patient dose and image quality optimization has been developed and implemented. It is based on a reference system acknowledged to yield acceptable image quality in a clinical trial. Two optimizations schemes have been studied, the first including the contrast of vessels as measure of image quality and the second scheme using also the signal-to-noise ratio of calcifications. Both schemes make use of our measure of useful dynamic range as a key quantity. A large variety of imaging conditions was simulated by varying the tube voltage, antiscatter device, screen-film system, and maximum optical density in the computed image. It was found that the optical density is crucial in screen-film chest radiography. Significant dose savings (30%-50%) can be accomplished without sacrificing image quality by using low-atomic-number grids with a low grid ratio or an air gap and more sensitive screen-film system. Dose-efficient configurations proposed by the model agree well with the example of good radiographic technique suggested by the European Commission.

Image Quality Assessment has diverse applications. A number of Image Quality measures are proposed, but none is proved to be true representative of human perception of image quality. We have subjectively investigated spectral distance based and human visual system based image quality measures for their effectiveness in representing the human perception for images corrupted with white noise. Each of the 160 images with various degrees of white noise is subjectively evaluated by 50 human subjects, resulting in 8000 human judgments. On the basis of evaluations, image independent human perception values are calculated. The perception values are plotted against spectral distance based and human visual system based image quality measures. The performance of quality measures is determined by graphical observations and polynomial curve fitting, resulting in best performance by Human Visual System Absolute norm.

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.

Safety and feasibility of cardiac FDG SPECT following oral administration of Acipimox, a nicotinic acid derivative: Comparison of image quality with hyperinsulinemic euglycemic clamping in nondiabetic patients

Now-a-days due to the huge increase in the size of image data Lossy Image Compression is highly used to reduce the image size but without having huge data loss. Image compression using SVD coding algorithm, Compressive Encoders and using prediction Error and Vectorization ratio are proved to have numerous application in image compression. Image compression using SVD coding algorithm involves refactoring of a digital image into three matrixes. Refactoring is achieved by using singular values, and the image is represented with a smaller set of values. Though, encoders cannot directly optimize due to the inherentNon-differentiability of the compression loss but it is out performing recently proposed approaches based on RNNs. The PE-VQ method is based on Prediction Error and Vector Quantisation techniques where image performance is determined using compression ratio and PSNR values using databases namely CLEF med 2009, Corel 1k and standard images like Lena, Barbara etc. Thus, in this research article a comparative study of these three techniquesis carried out where their image quality and compression ratio is examined by using the PSNR values and compression ratios.

BACKGROUND. Sequences with noncartesian k-space sampling may improve image quality of head and neck MRI. OBJECTIVE. The purpose of this study was to compare intraindividually the image quality of a spiral gradient-recalled echo (GRE) sequence and conventional cartesian GRE and cartesian turbo spin-echo (TSE) sequences for contrast-enhanced T1-weighted head and neck MRI. METHODS. This prospective study included patients referred for contrast-enhanced head and neck MRI from August 2020 to May 2021. Patients underwent 1.5-T MRI including contrast-enhanced spiral GRE (2 minutes 28 seconds), cartesian GRE (4 minutes 27 seconds), and cartesian TSE (3 minutes 41 seconds) sequences, acquired in rotating order across patients. Three radiologists independently assessed image quality measures, including conspicuity of prespecified lesions, using 5-point Likert scales. One reader measured maximal extent of dental material artifact and contrast-to-noise ratio (CNR). RESULTS. Thirty-one patients (13 men, 18 women; mean age, 63.8 years) were enrolled. Nineteen patients had a focal lesion; 22 had dental material. Interreader agreement for image quality measures was substantial to excellent (Krippendorff alpha, 0.681-1.000). Scores for overall image quality (whole head and neck, neck only, and head only), pulsation artifact, muscular contour delineation, vessel contour delineation, motion artifact, and differentiation between mucosa and pharyngeal muscles were significantly better for spiral GRE than for cartesian GRE and cartesian TSE for all readers (p < .05). Scores for lesion conspicuity (whole head and neck, neck only, and head only), quality of fat suppression, flow artifact, and foldover artifact were not significantly different between spiral GRE and the cartesian sequences for any reader (p > .05). Dental material artifact scores were significantly worse for spiral GRE than the other sequences for all readers (p < .05). The mean maximum extent of dental material artifact was 39.6 ± 25.5 (SD) mm for spiral GRE, 35.6 ± 24.3 mm for cartesian GRE, and 29.6 ± 21.4 mm for cartesian TSE; the mean CNR was 221.1 ± 94.5 for spiral GRE, 151.8 ± 85.7 for cartesian GRE, and 153.0 ± 63.2 for cartesian TSE (p < .001 between spiral GRE and other sequences for both measures). CONCLUSION. Three-dimensional spiral GRE improves subjective image quality and CNR of head and neck MRI with shorter scan time versus cartesian sequences, though it exhibits larger dental material artifact. CLINICAL IMPACT. A spiral sequence may help overcome certain challenges of conventional cartesian sequences for head and neck MRI.