Perceptual Visual Quality Research Articles

The Impact of Specialty Settings on the Perceived Quality of Medical Ultrasound Video

Health care professionals are increasingly viewing medical images and videos in a variety of environments. The perception of medical visual information across all specialties, career stages, and practice settings are critical to patient care and patient safety. Visual signal distortions, such as various types of noise and artifacts arising in medical imaging, affect the perceptual quality of visual content and potentially impact diagnoses. To optimize clinical practice, it is of fundamental importance to understand the way medical experts perceive visual quality. Psychophysical studies have been undertaken to evaluate the impact of visual distortions on the perceived quality of medical images and videos. However, very little research has been conducted on how speciality settings affect the perception of visual quality. In this paper, we investigate whether and how radiologists and sonographers differently perceive the quality of compressed ultrasound videos, via a dedicated subjective experiment. The findings can be used to develop useful solutions for improved visual experience and better image-based diagnoses.

Non-uniform Illumination Video Enhancement Based on Zone System and Fusion

In daily life, the acquisition of digital video will introduce non-uniform exposure due to the set of shooting equipment or scene illumination. Due to the low visibility, details are hidden and these videos usually fail to present visually pleasing browsing. Previous work typically relies on a single heuristic tone mapping curve to expand the dynamic range, which inevitably leads to uneven exposure. To solve this problem, we present a new video enhancement method based on zone system and image fusion. Given an input non-uniform illumination video, we first apply the zone system for exposure evaluation. We then remap each region using a series of tone mapping curves to generate multi-exposure regions which contain different exposed versions. Guided by some visual perception quality measures, we locate all the best exposed regions and then integrate them into a well-exposed video frame. Finally, in order to keep temporal consistency, we temporally propagate the zone regions from the former frame to the current frame. Experimental results have shown that the enhanced video exhibits uniform exposure, and preserves temporal consistency.

Simplification of 3D Graphics for Mobile Devices: Exploring the Trade-off Between Energy Savings and User Perceptions of Visual Quality

3D graphics have quickly become a popular form of media that can also be accessed with today’s mobile devices. However, the use of 3D applications with mobile devices is typically a very energy-consuming task due to the processing complexity and the large file size of 3D graphics. As a result, their use may lead to rapid depletion of the limited battery life. In this paper, we investigate how much energy savings can be gained in the transmission and rendering of 3D graphics by simplifying geometry data. In this connection, we also examine users’ perceptions on the visual quality of the simplified 3D models. The results of this paper provide new knowledge on the energy savings that can be gained through geometry simplification, as well as on how much the geometry can be simplified before the visual quality of 3D models becomes unacceptable for the mobile users. Based on the results, it can be concluded that geometry simplification can provide significant energy savings for mobile devices without disturbing the users. When geometry simplification is combined with distance based adjustment of detail, up to 52% energy savings were gained in our experiments compared to using only a single high quality 3D model.

A No Reference Image Quality Assessment Metric Based on Visual Perception

Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.

S-JND 모델을 사용한 주관적인 율 제어 알고리즘 기반의 HEVC 부호화 방법

본 논문에서는 인지 화질을 고려하기 위해 S-JND 모델 기반의 율 제어 알고리즘을 제안한다. 제안하는 율 제어 알고리즘은 인간이 가지는 시각 시스템의 특징을 반영하기 위하여 시각적 민감도와 시각적 관심도를 동시에 반영할 수 있도록 제작된 S-JND (Saliency-Just Noticeable Difference) 모델을 사용한다. 율 제어 알고리즘을 통해 비트를 분배하는 과정에서 픽쳐 내에 존재하는 각 CTU (Coding Tree Unit)가 가지는 S-JND threshold를 구한다. 각 CTU의 threshold는 적응적으로 적절한 비트를 분배하는데 사용되고, 따라서 제안하는 비트 분배 모델은 인지 화질을 향상 시킬 수 있다. 제안하는 방법의 성능 검증을 위해서 제안하는 방법을 HM 16.9에 구현하였으며, CTC (Common Test Condition) RA (Random Access), Low-delay B와 Low-delay P의 경우에 Class B와 Class C 영상들에 대해 실험 하였다. 실험 결과, 제안하는 방법은 기존 율 제어 알고리즘 대비 평균 2.3%의 비트율이 감소했고 BD-PSNR은 약 0.07dB 향상이 있었으며 비트 정확도 또한 0.06% 정도 증가하였다. DSCQS (Double Stimulus Continuous Quality Scale) 방법으로 측정한 결과, 제안하는 방법은 기존 방법 대비 0.03 MOS (Mean Opinion Score) 향상을 보였다. This paper proposes the rate control algorithm based on the S-JND (Saliency-Just Noticeable Difference) model for considering perceptual visual quality. The proposed rate control algorithm employs the S-JND model to simultaneously reflect human visual sensitivity and human visual attention for considering characteristics of human visual system. During allocating bits for CTU (Coding Tree Unit) level in a rate control, the bit allocation model calculates the S-JND threshold of each CTU in a picture. The threshold of each CTU is used for adaptively allocating a proper number of bits; thus, the proposed bit allocation model can improve perceptual visual quality. For performance evaluation of the proposed algorithm, the proposed algorithm was implemented on HM 16.9 and tested for sequences in Class B and Class C under the CTC (Common Test Condition) RA (Random Access), Low-delay B and Low-delay P case. Experimental results show that the proposed method reduces the bit-rate of 2.3%, and improves BD-PSNR of 0.07dB and bit-rate accuracy of 0.06% on average. We achieved MOS improvement of 0.03 with the proposed method, compared with the conventional method based on DSCQS (Double Stimulus Continuous Quality Scale).

Introducing oriented Laplacian diffusion into a variational decomposition model.

The decomposition model proposed by Osher, Solé and Vese in 2003 (the OSV model) is known for its good denoising performance. This performance has been found to be due to its higher weighting of lower image frequencies in the H −1-norm modeling the noise component in the model. However, the OSV model tends to also move high-frequency texture into this noise component. Diffusion with an oriented Laplacian for oriented texture is introduced in this paper, in lieu of the usual Laplacian operator used to solve the OSV model, thereby significantly reducing the presence of such texture in the noise component. Results obtained from the proposed oriented Laplacian model for test images with oriented texture are given, and compared to those from the OSV model as well as the Mean Curvature model (MCM). In general, the proposed oriented Laplacian model yields higher signal-to-noise ratios and visually superior denoising results than either the OSV or the MCM models. We also compare the proposed method to a non-local means model and find that although the proposed method generally yields slightly lower signal-to-noise ratios, it generally gives results of better perceptual visual quality.

Blind motion image deblurring using nonconvex higher-order total variation model

We propose a nonconvex higher-order total variation (TV) method for blind motion image deblurring. First, we introduce a nonconvex higher-order TV differential operator to define a new model of the blind motion image deblurring, which can effectively eliminate the staircase effect of the deblurred image; meanwhile, we employ an image sparse prior to improve the edge recovery quality. Second, to improve the accuracy of the estimated motion blur kernel, we use L1 norm and H1 norm as the blur kernel regularization term, considering the sparsity and smoothing of the motion blur kernel. Third, because it is difficult to solve the numerically computational complexity problem of the proposed model owing to the intrinsic nonconvexity, we propose a binary iterative strategy, which incorporates a reweighted minimization approximating scheme in the outer iteration, and a split Bregman algorithm in the inner iteration. And we also discuss the convergence of the proposed binary iterative strategy. Last, we conduct extensive experiments on both synthetic and real-world degraded images. The results demonstrate that the proposed method outperforms the previous representative methods in both quality of visual perception and quantitative measurement.

DCT-QM: A DCT-Based Quality Degradation Metric for Image Quality Optimization Problems

Recent development of computational image quality assessment methods has shown to give very promising results in measuring perceptual visual quality for distorted images. However, most of them are difficult to be applied for optimization problems due to the lack of desirable mathematical properties, such as differentiability, convexity, and valid distance metricability. This paper proposes a novel Discrete Cosine Transform (DCT)-based quality degradation metric, called DCT-QM, which is based on the probability summation theory with a psychometric function for neural responses in the receptive fields of visual cortex in psychophysics. Consequently, the DCT-QM is formulated as a weighted mean $\text{L}^{{{2}}}$ norm in the DCT domain, which is very easy to implement and inherits the three desirable mathematical properties, that is, differentiability, convexity, and valid distance metricability, for image quality optimization problems. The extensive experimental results show that the proposed DCT-QM has promising results for many practical distortion types in image processing problems by showing high consistency with perceived visual quality.

Pyramid coding based rate control for variable bit rate video streaming

Multi-pass encoding is preferred over single-pass encoding for applications such as television broadcasting and video on demand to obtain a consistent visual quality media. This study presents a novel pyramid coding based two-pass rate control scheme for variable bit rate video streaming. The proposed algorithm uses the statistics collected in the first pass to derive an optimal quantisation parameter for encoding the frames in the second pass. Simple linear estimation models are employed along with Gaussian filtering to update the frame quantisation parameter values in an adaptive manner, resulting in a smoother variation among these values and a better visual quality. The experimental results demonstrate that the proposed rate control scheme exhibits a better performance in terms of the peak signal-to-noise ratio and consistent perceptual visual quality in comparison with the existing rate control scheme in the joint model reference software while achieving the target bit rate.

SAR image de-noising based on texture strength and weighted nuclear norm minimization

As synthetic aperture radar (SAR) has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization (WNNM) is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis (PCA) and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures (BLS-GSM) method, non-local means (NLM) filtering in terms of both quantitative measure and visual perception quality.

Portable simultaneous vision device to simulate multifocal corrections

Multifocal lenses are increasingly used solutions for presbyopia, the age-related loss of crystalline lens focus ability. These lenses work by the principle of simultaneous vision, superimposing focused and defocused images on the retina. Providing the experience of simultaneous vision to a patient before permanent implantation of a multifocal lens is a recognized unmet need to increase the patient’s confidence and optimize the lens selection. We developed a hand-held, see-through multifocal vision simulator based on temporal multiplexing of a tunable lens. The device was calibrated and validated using focimetry and Hartmann–Shack aberrometry revealing high reproducibility of the through-focus multifocal energy distribution and high optical quality. We measured visual acuity and perceptual quality on nine cyclopeged patients with three monofocal, two bifocal, and two trifocal corrections with different far/intermediate/near energy distributions simulated using the device. Visual performance and perceptual quality with multifocal corrections varied across patients, although they were more uniform across distances than monofocal corrections. Among the bifocal and trifocal designs, a trifocal with more energy at far was the most frequently identified as providing better quality. The simultaneous vision simulator proved a promising compact tool to study visual performance with multifocal corrections and to select the lens design best suited for each patient, alternative to costly and bulky adaptive optics based devices.

Adaptive contrast enhancement using edge-based lighting condition estimation

This paper proposes a new approach to image contrast enhancement that improves the perceptual visual quality by considering the lighting condition and minimizing the structural distortion to a tolerable level. The proposed method consists of the following two major steps: lighting condition estimation and contrast enhancement processes. In the first step, the proposed method estimates the lighting condition by calculating the dynamic range along the edges of the image. In the second step, the method adaptively adjusts the luminance by considering both the estimated lighting condition and the order of luminance levels in order to improve the perceptual visual quality. In addition, the method properly reduces the structural distortion. Experimental results show that the proposed method improved the perceptual visual quality of various images by increasing the average structural fidelity, enhancement performance measure, entropy, and tone-mapped image quality index by up to 11%, 133%, 16%, and 11%, respectively, compared to the benchmark methods.

Toward a No-Reference Image Quality Assessment Using Statistics of Perceptual Color Descriptors.

Analysis of the statistical properties of natural images has played a vital role in the design of no-reference (NR) image quality assessment (IQA) techniques. In this paper, we propose parametric models describing the general characteristics of chromatic data in natural images. They provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions. The established models capture the correlation of chromatic data between spatially adjacent pixels by means of color invariance descriptors. The use of color invariance descriptors is inspired by their relevance to visual perception, since they provide less sensitive descriptions of image scenes against viewing geometry and illumination variations than luminances. In order to approximate the visual quality perception of chromatic distortions, we devise four parametric models derived from invariance descriptors representing independent aspects of color perception: 1) hue; 2) saturation; 3) opponent angle; and 4) spherical angle. The practical utility of the proposed models is examined by deploying them in our new general-purpose NR IQA metric. The metric initially estimates the parameters of the proposed chromatic models from an input image to constitute a collection of quality-aware features (QAF). Thereafter, a machine learning technique is applied to predict visual quality given a set of extracted QAFs. Experimentation performed on large-scale image databases demonstrates that the proposed metric correlates well with the provided subjective ratings of image quality over commonly encountered achromatic and chromatic distortions, indicating that it can be deployed on a wide variety of color image processing problems as a generalized IQA solution.

Underexposed Video Enhancement via Perception-Driven Progressive Fusion

Underexposed video enhancement aims at revealing hidden details that are barely noticeable in LDR video frames with noise. Previous work typically relies on a single heuristic tone mapping curve to expand the dynamic range, which inevitably leads to uneven exposure and visual artifacts. In this paper, we present a novel approach for underexposed video enhancement using an efficient perception-driven progressive fusion. For an input underexposed video, we first remap each video frame using a series of tentative tone mapping curves to generate an multi-exposure image sequence that contains different exposed versions of the original video frame. Guided by some visual perception quality measures encoding the desirable exposed appearance, we locate all the best exposed regions from multi-exposure image sequences and then integrate them into a well-exposed video in a temporally consistent manner. Finally, we further perform an effective texture-preserving spatio-temporal filtering on this well-exposed video to obtain a high-quality noise-free result. Experimental results have shown that the enhanced video exhibits uniform exposure, brings out noticeable details, preserves temporal coherence, and avoids visual artifacts. Besides, we demonstrate applications of our approach to a set of problems including video dehazing, video denoising and HDR video reconstruction.

A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception.

Computational models for image quality assessment (IQA) have been developed by exploring effective features that are consistent with the characteristics of a human visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize visual quality perception for local image characteristics and various distortion types. To solve this problem, we propose a new IQA method, called the structural contrast-quality index (SC-QI), by adopting a structural contrast index (SCI), which can well characterize local and global visual quality perceptions for various image characteristics with structural-distortion types. In addition to SCI, we devise some other perceptually important features for our SC-QI that can effectively reflect the characteristics of HVS for contrast sensitivity and chrominance component variation. Furthermore, we develop a modified SC-QI, called structural contrast distortion metric (SC-DM), which inherits desirable mathematical properties of valid distance metricability and quasi-convexity. So, it can effectively be used as a distance metric for image quality optimization problems. Extensive experimental results show that both SC-QI and SC-DM can very well characterize the HVS's properties of visual quality perception for local image characteristics and various distortion types, which is a distinctive merit of our methods compared with other IQA methods. As a result, both SC-QI and SC-DM have better performances with a strong consilience of global and local visual quality perception as well as with much lower computation complexity, compared with the state-of-the-art IQA methods. The MATLAB source codes of the proposed SC-QI and SC-DM are publicly available online at https://sites.google.com/site/sunghobaecv/iqa.

Visual perception in alpine ski racing

Alpine ski racing involves extreme conditions regarding speed, forces, and shuddering slopes. Contrary to other sports, the nature of visual perception in ski racing has hardly been explored. Consequently, focused interviews were conducted to gather information on the role of visual perception in alpine skiing and to identify relevant issues for future research. Twenty members of the Swiss national team were interviewed using a semi-structured interview guide focusing on athletes’ gaze behavior, factors affecting visual perception, and strategies to cope with perceptual difficulties. Subsequently, verbal data were analyzed by inductive content analysis according to Mayring to generate categories based on the interview material itself, instead of forcing data into existing theoretical frameworks. The athletes underlined the importance of visual perception for optimal performance. Even though they seem to rather unconsciously pick up visual information during races, general performance-related gaze strategies and visual cues could be identified. As factors affecting visual perception quality, perturbations, visibility, physical condition, alertness, time pressure, and mental condition were reported. On the athletes’ side, these factors are faced by strategies referring to race preparation, gaze behavior, or skiing technique. Finally, the analysis highlighted the fact that, in the eyes of the athletes, expertise in alpine skiing is determined by a strong mutual interdependency of perception and action. Our findings provide several starting points for future empirical research. In this regard, apart from gaze-related issues, interactions between visual perception and motor performance, e.g., for the optimization of handling perturbations, seem to be of particular interest.

Image Quality of Experience: A Subjective Test Targeting the Consumer’s Experience

This paper presents a subjective image quality experiment design that specifically targets understanding consumer perception of visual quality. With this consumer-oriented goal in mind, images were captured from a large number of representative consumer devices. This design choice is fundamentally different from previous work in the field, in that it is not based on simulated software impairments, but rather on naturally occurring artifacts due to the device and conditions. New subjective questions were posed, which allowed exploration of differences in the quality of experience (QoE) provided by various devices. This paper presents the experimental design implemented to capture ratings and feedback on these consumer devices. The study is analyzed to highlight and demonstrate the unique capabilities provided by this method.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Perceptual visual dysfunction, physical impairment and quality of life in Bangladeshi children with cerebral palsy

BackgroundCerebral palsy (CP) is the most common cause of motor disability in children and is often accompanied by sensory and/or cognitive impairment. The aim of this study was to characterise...

Public Perception of Visual Quality of Cut Mutia Mosque Park as Public Space in Jakarta

Cut Mutia Park is a city park which is an integral part and attached to the main courtyard of the mosque cut mutia. This park is a green open space that serves as a public space and generate the aesthetics of the city. There are seven critical elements that need to be studied to determine the public's perception of the park. Knowledgeable public perception of these elements, useful for city authorities in the development of city parks for future. The results of the research showed that of the seven elements studied, only two elements of which will be a positive perception, namely aspects of cleanliness and coolness, while five other aspects got a negative perception. As a result, generally Cut Mutia Parks research object only gets the value perception of -0.24. Details of the value obtained by each of these aspects are: -0.52 for comfortability; +0.13 for cleanliness; -0.003 for freshness; +0.26 for coolness; -0.77 for harmony level; -0.42 for beauty level; and -0.32 for interesting presented. Low perception from respondents indicated that Cut Mutia Park need better design such that it can give added value to the mosque and the environment surrounding. At the end can be said that the city authorities need to further improve attention in structuring of the park, to makes Cut Mutia Park become more comfortable, interesting and favored by the public, and also strengthening the power of the mosque as the central point.