Perceptual video quality assessment on a mobile platform considering both spatial resolution and quantization artifacts

This paper proposes a novel perceptual video quality assessment metric for streamed videos using optical flow statistical features. We analyze the impact of network losses on the decoded videos and the resulting error propagation. We show that the statistical features of the optical flow of the corrupted frames can be used to measure the distortion in the received video. We show that this approach is suitable for videos with complex motion patterns. Our technique does not make any assumptions on the coding conditions, network loss patterns or error concealment techniques. The proposed approach is pixel-based and relies only on the inconsistency of the optical flow of the corrupted frames. We validate our proposed quality metric by testing it on a variety of coded sequences subject to network losses from the recently proposed LIVE mobile database. Our results show that the proposed metric can estimate perceptual quality of channel-induced distortions at the frame and sequence levels. For the test videos, we report Pearson's and Spearman's correlation coefficients with the temporal mean opinion scores (MOSs) reported in the database. The results show average correlations of 0.91 and 0.92 for the test sequences, respectively.

The growing consumer interest in video communication has increased the users' awareness in the visual quality of the delivered media. This in turn increases, at the service provider end, the need for intelligent methodologies of optimal techniques for adapting to varying network conditions. Recent studies show that constraints on the bandwidth of transmission media should not always be translated to an increase in compression ratio to lower the bitrate of the video. Instead, a suitable option for adaptive streaming is to scale down the video temporally or spatially before encoding to maintain a desirable level of perceptual quality, while the viewing resolution is constant. Most of the existing studies to examine these scenarios are either limited to low resolution videos or lack in provisioning of subjective assessment of quality.We present here the results of our campaign of subjective quality assessment experiments done on a range of spatial and temporal resolutions, up to VGA and 30 frames per second respectively, under a number of bitrate conditions. The analysis shows, among other things, that keeping the spatial resolution is perceptually preferred among the three parameters that have impact on the video quality, even in the case with high temporal activity.

Although a variety of successful no-reference (blind) picture quality analyzers have been proposed, progress on the blind video quality analysis problem has been slow. We break down the problem of perceptual blind video quality assessment (VQA) into components, which we address individually, before proposing a holistic solution. The idea is to tackle the challenges that comprise the blind VQA problem individually in order to gain a better understanding of it. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.

Magnetic resonance imaging (MRI) has emerged as a powerful tool in medical diagnosis and research. Although high spatial resolution images are essential in medical diagnosis and image analysis, high temporal resolution is equally important in applications of dynamic contrast-enhanced MRI or functional brain MRI. In particular, in breast MRI the ability to differentiate between benign and malignant lesions depends, in part, on the temporal resolution of the dynamic image acquisition. New applications of MRI such as multi-feature analysis of image time series data and full 3D functional MRI or event-related functional MRI require high spatial and high temporal resolution for accurate image analysis on a voxel-by-voxel basis. Currently available partial Fourier reconstruction techniques, which effectively improve the time resolution, suffer from a reduced signal to noise ratio in the reconstructed image, a decrease in spatial resolution or reconstruction artefacts, making numerical image analysis difficult. In this work we present an image reconstruction algorithm based on image recovery theory which effectively doubles the temporal resolution and results in an image quality sufficient for further numerical analysis. The developed algorithm requires a full Fourier space acquisition of a pre-contrast or baseline image prior to the reconstruction procedure of the time series partial Fourier data.

Despite the burgeoning advances in 3D video technologies, 3D video adaptation is still in its infancy. 3D video has a multidimensional nature. To support the growth of 3D adaptation technologies, key factors that characterise this nature should be thoroughly studied. Spatial resolution is one of the important factors characterising this nature. Thus, particularly the effect of different spatial resolutions of depth maps on video quality and depth perception should be investigated. Similarly, the influence of the content related factors on the perception of different spatial resolutions of the depth maps should also be investigated. Therefore, evaluation studies are conducted using spatially scaled depth maps encoded at different bit rates and their original colour texture counterparts. The depth maps utilised in the evaluations present different spatial and motion complexity characteristics. The results demonstrate that when the spatial resolutions of the depth maps increase, both the video quality and depth perception improve. The amount of improvement is highly correlated with the structural and motion complexity characteristics of the depth maps.

To evaluate whether a dedicated, anatomically realistic knee phantom with orthopedic implants can be used to optimize clinical MRI sequences under realistic metallic conditions and to assess whether phantom-optimized sequences improve image quality compared with routine protocols. A 3D-printed knee phantom integrating a titanium screw and a stainless-steel fixation plate was developed. Phantom imaging was performed on a clinical 1.5T MRI system. In this exploratory proof-of-concept study, T1w and STIR sequences were iteratively optimized using the phantom and compared with vendor-default and routine clinical protocols. The phantom-optimized sequences were subsequently applied in three asymptomatic volunteers with metallic knee implants and compared with routine sequences. Image quality was independently assessed by four blinded readers using a 5-point Likert scale across spatial resolution, artifact reduction, and overall image quality. Phantom-optimized sequences achieved significantly higher image quality scores compared with routine protocols. Improvements were consistent across all three predefined criteria: spatial resolution (mean increase +0.70 points, p < 0.001), artifact reduction (+0.65, p < 0.001), and overall image quality (+0.78, p < 0.001). These gains were observed for both T1-weighted and STIR acquisitions without extending acquisition times. Phantom-guided optimization provides a reproducible, patient-independent framework for tuning MRI protocols near orthopedic hardware. Anatomically realistic phantoms represent a promising methodological tool for developing, testing, and standardizing MRI sequences under controlled and clinically relevant conditions.

Background Diffusion-weighted imaging (DWI) is a widely used magnetic resonance imaging method for non-invasive assessment of tissue structure. However, conventional Diffusion-weighted imaging (C-DWI) often suffers from low spatial resolution and artifacts. This systematic review evaluates the technical and clinical benefits of Reduced Field-of-View Diffusion-weighted magnetic resonance imaging (rFOV DWI) in comparison to C-DWI techniques. Objective The aim of the present review was to systematically evaluate the technical advances, diagnostic accuracy, and clinical applications of rFOV DWI compared with C-DWI techniques. Study design and methods A systematic review was performed according to PRISMA guidelines. Databases including ScienceDirect, Springer, Elsevier, PubMed/Medline, Wiley Online Library, and Scopus were searched. Eleven eligible studies were identified and reviewed for imaging efficacy, artifact reduction, spatial resolution, and diagnostic utility. Results rFOV DWI significantly improves image clarity by reducing susceptibility and motion artifacts. It enables higher spatial resolution and more accurate visualization of small anatomical structures. Compared to C-DWI, rFOV DWI shows enhanced diagnostic precision, which supports early disease detection and more informed clinical decisions. Conclusion rFOV DWI represents a notable advancement in imaging, offering improved quality and clinical utility. Its enhanced performance supports its integration into routine diagnostic workflows and highlights its potential for broader application as MRI technologies evolve.

The temporal resolution of x-ray computed tomography (CT) is limited by the scanner rotation speed and detector readout time. One way to reduce the detector readout time is to acquire fewer number of projections. However, reconstruction using sparse-view data could result in spatial resolution loss and reconstruction artifacts that may negatively affect the clinical diagnoses. Therefore, improving the spatial resolution of sparse-view CT (SVCT) is of great practical value. In this study, we proposed a deep learning-based approach for SVCT spatial resolution enhancement. The proposed method utilizes a densely connected convolutional neural network (CNN) that is further aided by a radial location map to recover the radially dependent blurring caused by the continuous rotation of an x-ray source. The proposed method was evaluated using sparse-view data synthesized from full-view projection data of real patients. The results showed that the proposed CNN was able to recover the resolution loss and improve the image quality. Compared with the network using the same main structure but without a radial location map, the proposed method achieved better image quality in terms of the mean absolute error and structure similarity.

Although a significant research effort has been carried out for investigating 3 Dimensional (3D) video display, transmission, coding, etc, the same is not applicable for 3D video adaptation. In addition, ambient illumination, spatial resolution and 3D video content related contexts have not been particularly considered in a hybrid manner for the 3D video adaptation purpose in literature to date. In this paper, an adaptation decision taking technique is designed to predict the bit rate of 3D video sequences to be adapted by a proposed adaptation model. The ambient illumination condition of the viewing environment is considered in these proposed technique and model together with spatial resolution, video quality, and depth perception related contexts of the 3D video. Experimental results derived by the assistance of subjective experiments prove that the proposed model is quite efficient to adapt the 3D video sequences without compromising the 3D video perception of the users.

To interpret the impressions of observers, it is necessary to understand the relationship between components that influence perceived video quality. This paper addresses the effect of assessment methodology on the subjective judgement for spatial and temporal impaired video material, caused by video adaptation methods that come into play when there is variable throughput of video material (I-Frame Delay and Signal-to-Noise Ratio scalability). Judgement strategies used are the double-stimulus continuous-quality scale (DSCQS) and the double stimulus impairment scale (DSIS). Results show no evidence for an influence of spatial artifacts on perceived video quality with the presented judgement strategies. Results for the influence of temporal artifacts are less easy to interpret, because it is not possible to distinguish whether the non-linear relation between DSIS and DSCQS appeared because of the temporal artifacts themselves or presented scene content.

The goal of this work is to discuss the influence of spatial and temporal quantization on the modulation transfer function of the X-ray image in digital scanning X-ray machines designed based on matrix linear receivers operating in the TDI (Time Delay Integration) mode.

The Advanced WiFS sensor of RESOURCESAT- 1 satellite offers significantly improved specifications compared to the WiFS sensor onboard IRS IC, P3 and ID satellites. The improvements are in terms of spatial resolution, radiometry (quantisation levels) and number of spectral bands. In the present study, an attempt has been made to quantify the gains due to these enhanced specifications. The study has been carried out in a predominantly agricultural area. For the study reported here, one set of overlapping data acquired on the same day by WiFS and AWiFS sensors has been selected. This eliminates the need of atmospheric correction/ normalization for comparison. The effect of spatial resolution has been studied by applying ISODATA spectral clustering algorithm with number of clusters set at three different levels, viz., 10, 20 and 30. They are assumed to mimic first, second and third level classification, respectively. Output images were filtered using 3 × 3 majority filter. Homogeneous polygons having area less than 1/2 and 1 pixel of WiFS were recorded. This indicates the minimum loss by using WiFS data. A relative gain of 10 – 15 % is observed due to improvement in spatial resolution. For comparison of radiometry, local variance measure was used. It was observed that local variance is much larger for AWiFS data in comparison with WiFS data. This indicates presence of enhanced local contrast, hence heterogeneity, in AWiFS data over WiFS data. Separability analysis has been carried out to demonstrate improvements due to two additional spectral bands (Green and SWIR).

Three-dimensional (3D) observation of moving cells offers critical insights into fundamental biological processes. However, the limited temporal resolution of traditional scanning-based 3D imaging techniques constrains their use in live-cell imaging. Light-field microscopy (LFM) has emerged as a scanning-free alternative, enabling rapid 3D volumetric data acquisition. Despite this advantage, LFM's spatial resolution and reconstruction artifacts remain challenges that hinder its broader application in biological imaging. This study proposes a custom-designed staggered bifocal microlens array (MLA), fabricated through a cost-effective, high-throughput method, to overcome these limitations. This approach enhances spatial resolution while minimizing reconstruction artifacts compared to conventional uniform MLAs. We demonstrate the technique through 3D imaging of microbeads at varying depths and live cells suspended in droplets. This bifocal MLA presents a promising advancement for extending LFM's applications in biological research.

Digital breast tomosynthesis is a novel breast cancer detection technique by allowing the reconstruction of arbitrary planes in the breast from a set of limited-angle projection images acquired at different view angles with a particular tube geometry setup. In this paper, filtered backprojection (FBP) was optimized as the reconstruction method with a parallel imaging breast tomosynthesis system. The slice thickness filter (profile filter) with task-adapted parameters was applied to computer simulated data, in order to investigate the main effects of this kind of filter regarding spatial resolution and artifacts in the reconstructed results.

Conventionally, digital halftoning is accomplished by either changing the size of printed dots or changing the relative density of dots on the page. These two approaches are analogous to amplitude modulation (AM) or frequency modulation (FM) used in communications. A typical AM halftoning method, such as cluster dot screening, has very low computational requirements and good print stability. However, it suffers from low spatial resolution and Moire artifacts. Alternatively, popular FM halftoning methods, such as error diffusion, can achieve high spatial resolution and are free of Moire artifacts but lack the print stability required for electro-photographic printing. In this paper, we present a new class of halftoning algorithms that simultaneously modulate both the size and density of printed dots. We call this new class of algorithms AM/FM halftoning. The major advantages of AM/FM halftoning are: (1). Better stability in shadow area than dispersed dot methods through the formation of larger dot clusters. (2). Better Moire resistance than clustered dot screens through irregular dot placement. (3)The ability to systematically optimize dot size and density to produce the best possible print quality at each gray level. A specific implementation of AM/FM halftoning is developed for use with electro-photographic printers having pulse width modulation (PWM) technology. We present results using dot size and dot density curves obtained through measurement-based optimization, and demonstrate that AM/FM halftoning achieves high spatial resolution, smooth halftone textures, good printing stability, and Moire resistances.