Comparative Analysis of Daylight and Visual Quality Across School Plan Typologies: A Spatial Layout Perspective

To examine the performance of state-of-the-art wavefront-guided scleral contact lenses (wfgSCLs) on a sample of keratoconic eyes, with emphasis on performance quantified with visual quality metrics, and to provide a detailed discussion of the process used to design, manufacture, and evaluate wfgSCLs. Fourteen eyes of seven subjects with keratoconus were enrolled and a wfgSCL was designed for each eye. High-contrast visual acuity and visual quality metrics were used to assess the on-eye performance of the lenses. The wfgSCL provided statistically lower levels of both lower-order root mean square (RMS) (p < 0.001) and higher-order RMS (HORMS) (p < 0.02) than an intermediate spherical equivalent scleral contact lens. The wfgSCL provided lower levels of lower-order RMS than a normal group of well-corrected observers (p << 0.001). However, the wfgSCL does not provide less HORMS than the normal group (p = 0.41). Of the 14 eyes studied, 10 successfully reached the exit criteria, achieving residual HORMS wavefront error less than or within 1 SD of the levels experienced by normal, age-matched subjects. In addition, measures of visual image quality (logVSX, logNS, and logLIB) for the 10 eyes were well distributed within the range of values seen in normal eyes. However, visual performance as measured by high-contrast acuity did not reach normal, age-matched levels, which is in agreement with prior results associated with the acute application of wavefront correction to keratoconic eyes. Wavefront-guided scleral contact lenses are capable of optically compensating for the deleterious effects of higher-order aberration concomitant with the disease and can provide visual image quality equivalent to that seen in normal eyes. Longer-duration studies are needed to assess whether the visual system of the highly aberrated eye wearing a wfgSCL is capable of producing visual performance levels typical of the normal population.

This paper studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise removal often results in image details loss or smoothing. A new test image database, FLT, containing 75 noise-free "reference" images and 300 filtered ("distorted") images is developed. Each reference image, corrupted by an additive white Gaussian noise, is denoised by the BM3D filter with four different values of threshold parameter (four levels of noise suppression). After carrying out a perceptual quality assessment of distorted images, the mean opinion scores (MOS) are obtained and compared with the values of known full reference quality metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC) between PSNR values and MOS has a value close to zero, and SROCC between values of known full-reference image visual quality metrics and MOS does not exceed 0.82 (which is reached by a new visual quality metric proposed in this paper). The FLT dataset is more complex than earlier datasets used for assessment of visual quality for image denoising. Thus, it can be effectively used to design new image visual quality metrics for image denoising.

To improve the prediction accuracy of visual quality metrics for video we propose two simple steps: temporal pooling in order to gain a set of parameters from one measured feature and a correction step using videos of known visual quality. We demonstrate this approach on the well known PSNR. Firstly, we achieve a more accurate quality prediction by replacing the mean luma PSNR by alternative PSNR-based parameters. Secondly, we exploit the almost linear relationship between the output of a quality metric and the subjectively perceived visual quality for individual video sequences. We do this by estimating the parameters of this linear relationship with the help of additionally generated videos of known visual quality. Moreover, we show that this is also true for very different coding technologies. Also we used cross validation to verify our results. Combining these two steps, we achieve for a set of four different high definition videos an increase of the Pearson correlation coefficient from 0.69 to 0.88 for PSNR, outperforming other, more sophisticated full-reference video quality metrics.

<title>Abstract</title> The design of shading devices and the window-to-wall ratio are critical elements in enhancing visual comfort, productivity, and well-being within buildings. Achieving optimal levels of Useful Daylight while lowering thermal discomfort from overheating and visual discomfort from glare can be accomplished by controlling the design and configuration of shade systems. In-depth assessments of classrooms with various façade configurations were conducted using a simulation-based methodology and parametric tools, including Rhino, Grasshopper, Ladybug, Honeybee, Wallacei, and Galapagos. This research evaluates multiple façade configurations, focusing on metrics like Daylight Autonomy (DA), Useful Daylight Illuminance (UDI), and Daylight Glare Probability (DGP). The study focuses on assessing the regional applicability of responsive façade designs for lecture halls and classrooms in Egyptian universities. Climate data from different regions (Giza, Aswan, Alexandria, and Hurghada) is used to analyze the theoretical model's adaptability. A single and multi-objective optimization framework is used to compare the performance of optimized shading designs in diverse climates, identify regions where responsive façades yield the most effective balance between daylight availability and glare control, and determine the regional suitability and adaptability of proposed responsive façade strategies. Comparative analysis and findings are then compared between different climatic zones and façade types, identifying the most effective designs for balancing visual comfort and energy efficiency in hot, dry, and humid climates. The study provides design recommendations for future educational buildings in Egypt. The multi-objective optimization focused on (DA), (UDI), (DGP), and the quality view (QV). Hurghada was found to be the most effective area for responsive façade applications in educational buildings due to its balance of good visual comfort, low glare, and high UDI. West Cairo came in second, but its modest QV and sporadic near-glare thresholds suggest the need for adaptive shade solutions. Aswan exhibited greater variance in QV and a higher risk of overheating, necessitating the use of thermal mass and green techniques. Alexandria was not the ideal place for replicating a default façade without sophisticated glare mitigation measures.

Daylighting has become an essential feature in libraries since it can boost productivity, well-being, and energy savings. It is crucial to prevent discomfort glare irritation while maintaining the quality of view, and daylight demands. This study compares four different shading systems on all fronts of a reading room. Shading devices include a Perforated Solar Screen (PSS), besides three Venetian Blinds typologies (exterior, interior, and exterior integrated with overhangs) are generated considering various parameters. The 60-min time step for an entire year is used to find out the worst dates for glare analysis in different positions for Rasht, Iran. The findings of Daylight Autonomy (DA), Useful Daylight Illuminance (UDI), Daylight Glare Probability (DGP), and Quality of View (QV) depict that PSS is ineffective at glare reduction but is adequate for annual metrics and view quality. Moreover, Venetian Blinds in every three typologies are sufficient to deal with glare problems on most dates, despite differences in their characteristics being reported. Annual metrics and Quality of View in these cases, depending on variables, are changeable.

Noise is one of the main factors that degrade image visual quality. Assessment of perceptual quality of noisy images is of critical importance for imaging systems and image processing application. In this paper, we propose a framework that employs multi-layer neural networks to predict visual quality for noisy images. In particular, the proposed method utilizes quite simple features extracted in discrete cosine transform (DCT) domain for predicting values of full-reference visual quality metrics for noisy images. The neural networks are trained and tested on noisy images corrupted by additive white Gaussian noise (AWGN). The experimental results show that prediction is quite accurate and fast. Source codes of the proposed method and datasets will be available at https://github.com/ViA-RiVaL/Blind-Noisy-Image-Visual-Quality-Prediction.

Background: During clinical subjective refraction, sphere, cylinder and axis are adjusted to achieve the best corrected acuity using patient responses. Certain visual quality metrics are highly correlated with acuity, potentially allowing better objective refractions by objectively answering the question “which is better, 1 or 2.”

Remote sensing images are subject to complex noise and distortions, which reduce their quality and can lead to loss of information. Visual quality metrics can be applied to automate and optimize remote sensing image processing. The problem of taking into account distortions typical for remote sensing data in existing test image databases is considered in this paper. The efficiency of 50 full-reference quality metrics for typical remote sensing distortions is estimated. The paper proposes a robust combined metric based on alpha-trimmed mean. The influence of linearization and the use of various correlation coefficients on the formation of a robust metric are studied. The effectiveness of the solution is tested using the cross-database approach based on the test image databases including TID2013, KADID10k, MDID and LIVE Multiply Distorted Image Quality Database.

While Denoising is an extensively studied task in signal processing research, most denoising methods are designed and evaluated using readily processed image data, e.g. the well-known Kodak data set. The noise model is usually additive white Gaussian noise (AWGN). This kind of test data does not correspond to nowadays real-world image data taken with a digital camera. Using such unrealistic data to test, optimize and compare denoising algorithms may lead to incorrect parameter tuning or suboptimal choices in research on real-time camera denoising algorithms. In this paper we derive a precise analysis of the noise characteristics for the different steps in the color processing. Based on real camera noise measurements and simulation of the processing steps, we obtain a good approximation for the noise characteristics. We further show how this approximation can be used in standard wavelet denoising methods. We improve the wavelet hard thresholding and bivariate thresholding based on our noise analysis results. Both the visual quality and objective quality metrics show the advantage of the proposed method. As the method is implemented using look-up-tables that are calculated before the denoising step, our method can be implemented with very low computational complexity and can process HD video sequences real-time in an FPGA.

Color images formed by modern digital cameras are often noisy, especially if they are captured in bad illumination conditions. This makes desirable to remove the noise by image pre-filtering. A specific feature of the noise observed for the considered application is that it can be spatially correlated. Filters to be applied have to effectively suppress noise introducing only negligible distortions into processed images. Moreover, such filters have to be fast enough and tested for a variety of natural images and noise properties. Another specific requirement is that a visual quality of processed images has to be paid a specific attention. To carry out intensive testing of some denoising approaches, a recently designed database TID2008 of distorted images provides a good opportunity since it contains 25 different images corrupted by i.i.d. and spatially correlated noise with several levels of variances. Taking into account the known fact that the color components are highly correlated, both modern 2D (component-wise) and 3D (vector) filtering techniques are studied. It is demonstrated that the use of 3D filters that allow exploiting inter-channel correlation provides considerably better results in terms of conventional and visual quality metrics. It is also shown how 3D filter based on discrete cosine transform (DCT) can be adapted to a spatial correlation of noise. This adaptation produces sufficient increase of the filter's efficiency. Examples of filter's performance are presented.

Many visual quality metrics are used to measure the quality of lossy compressed images and videos, and are integrated in the rate-distortion optimization of hybrid video codecs. However, most of the metrics focus on the average objective quality in a picture. In certain applications, like medical image processing, the maximum absolute error should be more weighted. In this paper, the rate-distortion optimization of HEVC is extended by integrating this error metric. Thus, rate, average error, and maximum absolute error are jointly optimized. Furthermore, a weighting factor α is included into the calculation of the optimization for balancing the ratio between average and maximum absolute error. For HEVC intra with $\alpha =0.25$ an average maximum absolute error reduction of −25.63 can be achieved, while the bitrate increases slightly by 0.59%. Furthermore, the visual quality of the medical volumes improves and the data fidelity increases, i.e. less block artifacts appear and less structure disappear.

To compare ocular and internal aberrations after femtosecond laser anterior capsulotomy and continuous curvilinear capsulorrhexis in cataract surgery. In this prospective study, anterior capsulotomy was performed during cataract surgery with an intraocular femtosecond (FS) laser (Alcon LenSx Inc) in 48 eyes. As a control group, continuous curvilinear capsulorrhexis (CCC) was performed in 51 eyes. Wavefront aberrometry, corneal topography, and objective visual quality were measured using the OPD-Scan (NIDEK Co Ltd). Vertical and horizontal tilt, coma, and visual quality metrics were evaluated separately to determine whether the source of aberrations was ocular or internal. Main outcome measures included postoperative residual refraction, uncorrected and corrected visual acuities, ocular and internal aberrations, Strehl ratio, and modulation transfer function (MTF). No statistically significant differences were noted between the FS and CCC groups, respectively, in postoperative sphere (-0.60 ± 1.50 vs -0.50 ± 1.40 diopters [D]), postoperative cylinder (1.30 ± 1.01 vs 1.10 ± 1.10 D), uncorrected distance visual acuity (0.86 ± 0.15 vs 0.88 ± 0.08), or corrected distance visual acuity (0.97 ± 0.08 vs 0.97 ± 0.06). The FS group had significantly lower values of intraocular vertical tilt (-0.05 ± 0.36 vs 0.27 ± 0.57) and coma (-0.003 ± 0.11 vs 0.1 ± 0.15), and significantly higher Strehl ratios (0.02 ± 0.02 vs 0.01 ± 0.01) and MTF values at all measured cycles per degree, compared to the CCC group. Capsulotomy performed with an intraocular FS laser induced significantly less internal aberrations measured by the NIDEK OPD-Scan aberrometer compared to eyes that underwent CCC, which may result in better optical quality after the procedure.

We address lossy compression of noisy remote sensing images, where the noise is supposed to be spatially uncorrelated (white), additive originally or after a proper variance-stabilizing transformation (VST). In such situations, the so-called optimal operation point (OOP) might exist. The OOP is associated with the parameter that controls compression (e.g., quantization step) for which the compressed image is the closest to the noise-free image according to a certain criterion and is closer than the original noisy image. Lossy compression in the neighborhood of OOP, if it exists, relates to an essential noise filtering effect and some distortions. Then such lossy compression (in the neighborhood of OOP) becomes expedient. However, it may be that OOP does not exist for a given image and the observed noise intensity. In such a situation, it can be reasonable to carry out a more “careful” image compression (with a lower compression ratio). Also, it is expedient to predict the existence of OOP and the compression parameters at this point in advance in order to perform adaptive and automated compression. The OOP existence that can be predicted for some coders based on the discrete cosine transform (DCT) is shown. The proposed prediction procedure is simple and fast. It presumes the calculation of DCT coefficient statistics in nonoverlapping 8×8 pixel blocks for a given image and uses an approximating curve obtained in advance. It is shown that it is possible to predict values for both conventional metrics, such as mean square error or peak-signal-to-noise ratio, and some visual quality metrics for the coder parameters that correspond to a possible OOP. The designed prediction procedure is tested on Hyperion and AVIRIS hyperspectral remote sensing data.

With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatiotemporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance.

Steganography, a technique for concealing information, often faces challenges such as low decoding accuracy and inadequate extraction of edge and global features. To overcome these limitations, we propose a dual-stream U-Net framework with integrated edge enhancement for image steganography. Our main contributions include the adoption of a dual-stream U-Net structure in the encoder, integrating an edge-enhancement stream with the InceptionDMK module for multi-scale edge detail extraction, and incorporating a multi-scale median attention (MSMA) module into the original input stream to enhance feature representation. This dual-stream design promotes deep feature fusion, thereby improving the edge details and embedding capacity of stego images. Moreover, an iterative optimization strategy is employed to progressively refine the selection of cover images and the embedding process, achieving enhanced stego quality and decoding performance. Experiments show that our method produces high-quality stego images across multiple public datasets, achieving near-100% decoding accuracy. It also surpasses existing methods in visual quality metrics like PSNR and SSIM. This framework offers a promising approach for enhancing steganographic security in real-world applications such as secure communication and data protection.