Spatial Distortion Research Articles

Spatial memory distortions for the shapes of walked paths occur in violation of physically experienced geometry.

An important question regards the nature of our spatial memories for the paths that we have walked and, in particular, whether such distortions might violate the topological properties of the shape of the paths (i.e., creating an intersection when two paths did not intersect or vice versa). To investigate whether and how this might occur, we tested humans in situations in which they walked simple paths and idiothetic and visual cues either matched or mismatched, with the mismatching cues creating the greatest potential for topological distortions. Participants walked four-segment paths with 90° turns in immersive virtual reality and pointed to their start location when they arrived at the end of the path. In paths with a crossing, when the intersection was not presented, participants pointed to a novel start location suggesting a topological distortion involving non-crossed paths. In paths without a crossing, when a false intersection was presented, participants pointed to a novel start location suggesting a topological distortion involving crossed paths. In paths without crossings and without false intersections, participants showed reduced pointing errors that typically did not involve topological distortions. Distortions more generally, as indicated by pointing errors to the start location, were significantly reduced for walked paths involving primarily idiothetic cues with limited visual cues; conversely, distortions were significantly increased when idiothetic cues were diminished and navigation relied primarily on visual cues. Our findings suggest that our spatial memories for walked paths sometimes involve topological distortions, particularly when resolving the competition between idiothetic and visual cues.

Corrigendum: 9‐2e: The effect of spatial dynamic distortions on visually induced motion sickness: a pilot study on the head‐mounted displays

Corrigendum: 9‐2e: The effect of spatial dynamic distortions on visually induced motion sickness: a pilot study on the head‐mounted displays

Blind Video Quality Assessment for Ultra-High-Definition Video Based on Super-Resolution and Deep Reinforcement Learning.

Ultra-high-definition (UHD) video has brought new challenges to objective video quality assessment (VQA) due to its high resolution and high frame rate. Most existing VQA methods are designed for non-UHD videos-when they are employed to deal with UHD videos, the processing speed will be slow and the global spatial features cannot be fully extracted. In addition, these VQA methods usually segment the video into multiple segments, predict the quality score of each segment, and then average the quality score of each segment to obtain the quality score of the whole video. This breaks the temporal correlation of the video sequences and is inconsistent with the characteristics of human visual perception. In this paper, we present a no-reference VQA method, aiming to effectively and efficiently predict quality scores for UHD videos. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet), which can quickly extract the global spatial distortion features of UHD videos. Then, to aggregate the spatial distortion features of each UHD frame, we propose a time fusion network based on a reinforcement learning model (RL-TFNet), in which the actor network continuously combines multiple frame features extracted by SR-SDFNet and outputs an action to adjust the current quality score to approximate the subjective score, and the critic network outputs action values to optimize the quality perception of the actor network. Finally, we conduct large-scale experiments on UHD VQA databases and the results reveal that, compared to other state-of-the-art VQA methods, our method achieves competitive quality prediction performance with a shorter runtime and fewer model parameters.

Managing deviant data in spatial frequency response (SFR) measurement by outlier rejection

The edge-based Spatial Frequency Response (e-SFR) method was first developed for evaluating camera image resolution and image sharpness. The method was described in the first version of the ISO 12233 standard. Since then, the method has been applied in a wide range of applications, including medical, security, archiving, and document processing. However, with this broad application, several of the assumptions of the method are no longer closely followed. This has led to several improvements aimed at broadening its application, for example for lenses with spatial distortion. We can think of the evaluation of image quality parameters as an estimation problem, based on the gathered data, often from digital images. In this paper, we address the mitigation of measurement error that is introduced when the analysis is applied to low-exposure (and therefore, noisy) applications and those with small analysis regions. We consider the origins of both bias and variation in the resulting SFR measurement and present practical ways to reduce them. We describe the screening of outlier edge-location values as a method for improved edge detection. This, in turn, is related to a reduction in negative bias in the resulting SFR.

Oceanographic controls on Southern Ocean dimethyl sulfide distributions revealed by machine learning algorithms

AbstractWe developed two machine learning models to map the Southern Ocean distribution of the climate‐active gas dimethyl sulfide (DMS) at 20 km resolution. Results obtained from ensembles of random forest regressions and artificial neural networks reproduce observed DMS distributions with significantly higher accuracy than traditional statistical techniques, and are less prone to biases and spatial distortions than existing interpolation‐based climatologies. Both models predict persistently low offshore DMS concentrations associated with the Antarctic circumpolar current, suggesting that wind‐driven overturning mixing is the dominant regional control on DMS distributions. In addition, 60% of the variance in DMS seasonality is explained by changes in mixed layer depth and sea surface temperature, with a significant correlation between DMS concentrations and sea‐ice cover in coastal waters. We further identify the tracer Si* (defined as ) as a potentially important predictor for regional DMS distributions in Southern Ocean waters. At finer scales, our models capture various oceanographic features, including eddies, hydrographic fronts and jets that appear to play a role in driving DMS variability. Our results yield an estimated Southern Ocean sea‐air DMS flux of 8.7 ± 2.1 Tg S integrated across the phytoplankton growing season (October to April), representing 30.8% of total global oceanic S emissions, and highlighting the region's importance to the marine sulfur cycle. Our work provides new insights into the drivers of spatial variability in Southern Ocean DMS concentrations and sea‐air fluxes, and their potential responses to future climate‐dependent changes.

Greenhouse gases monitoring instrument on a GF-5 satellite-II: correction of spatial and frequency-dependent phase distortion.

Interference data obtained by a spatial heterodyne spectrometer (SHS) is subject to various error factors and suffers from complex phase distortion. Traditional phase correction methods, such as the Amplitude, Merzt, and Forman methods, extract phase distortion in the spectral domain and correct it, which cannot effectively correct spatial phase distortion. Through theoretical derivation and numerical simulation, the spatial phase distortion is firstly determined and corrected in the interference domain. The frequency-dependent phase distortion is then extracted in the spectral domain and corrected. This novel phase distortion correction method named the phase decomposition method was applied to the in-orbit interference data of Greenhouse gases Monitoring Instrument-II (GMI-II). Compared with traditional phase correction methods, the root-mean-square error of the spectrum corrected using the phase decomposition method is reduced by 81.37%.

Effect of spatial distortions in head-mounted displays on visually induced motion sickness.

Incomplete optical distortion correction in VR HMDs leads to spatial dynamic distortion, which is a potential cause of VIMS. A perception experiment is designed for the investigation with three spatial distortion levels, with the subjective SSQ, five-scale VIMS level rating, and objective postural instability adopted as the evaluation metrics. The results show that the factor of spatial distortion level has a significant effect on all metrics increments (p<0.05). As the spatial distortion level drops off, the increments of VIMS symptoms decrease. The study highlights the importance of perfect spatial distortion correction in VR HMDs for eliminating the potential VIMS aggravation effect.

Refractive symmetry analysis of fellow eyes

The biological structure of human body frequently exhibit symmetry. The hypothesis of refractive symmetry assumes that the two eyes of an individual should have homogeneous refractive properties. Optometry clinical management mostly rely upon meridional magnification effect, spatial distortion, and binocular spatial perception while prescribing refractive correction. Additionally, bilateral symmetry analysis approach has proven very informative for better diagnosis. This study was multi-centered, cross-sectional, analytical, and retrospective, where we studied refractive errors with conventional clinical notation using the sphere, cylinder, and axis. The collected data were scrutinized using the SPSS, Minitab Statistical Software. In inter-ocular symmetry analysis, if the numerical difference between both axes is zero, Equal Octant or Direct Symmetry is to be considered. While, with Mirror Octants or Mirror Symmetry or Enantiomorphism, the axes difference is not zero, but the portrayal of the figures are reverberatory. 53.23% and 70.15% of the cases rooted in the Equal Octant and Mirror Octant pattern respectively, within 15° of absolute symmetry. Analysis showed that age between 7 to 14 years has the lowest inter-ocular axis difference with both the models. The average difference in inter-ocular axis was significantly reduced in the isorule group compared to the anisorule group, as observed in both direct and mirror symmetry models. Interval plot shows a lower interval proximity for Mirror Octant, in compare to Equal Octant. We tracked down that isorule bilateral astigmatism dominates the study population. Whereas, Mirror Octant is more periodic than Equal Octant.

CONVIQT: Contrastive Video Quality Estimator.

Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Distortion type identification and degradation level determination is employed as an auxiliary task to train a deep learning model containing a deep Convolutional Neural Network (CNN) that extracts spatial features, as well as a recurrent unit that captures temporal information. The model is trained using a contrastive loss and we therefore refer to this training framework and resulting model as CONtrastive VIdeo Quality EstimaTor (CONVIQT). During testing, the weights of the trained model are frozen, and a linear regressor maps the learned features to quality scores in a no-reference (NR) setting. We conduct comprehensive evaluations of the proposed model against leading algorithms on multiple VQA databases containing wide ranges of spatial and temporal distortions. We analyze the correlations between model predictions and ground-truth quality ratings, and show that CONVIQT achieves competitive performance when compared to state-of-the-art NR-VQA models, even though it is not trained on those databases. Our ablation experiments demonstrate that the learned representations are highly robust and generalize well across synthetic and realistic distortions. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.

Моделирование алгоритма повышения разрешающей способности гиперспектральной системы

The article deals with the issues of computer simulation of algorithms for processing spectral images, namely: the procedure for increasing their clarity. For this purpose, a spectral image obtained from a hyperspectrometer is used, as well as additional information from a high-resolution color video camera. The hyperspectrometer and the color video camera form a hyperspectral system in which various processing procedures can be implemented. The article considers a relatively simple processing algorithm with use of E. Land's retinex theory. A block diagram of the image processing stages is presented, as well as the results of computer modeling using real spectral images. Comparative quantitative characteristics of reference and processed images are analyzed. It is shown by modeling that with insignificant spatial distortions, the number of singular points (gradients) of the image, and, consequently, the clarity of the original spectral image increase by several times.

Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video.

Blind visual quality assessment (BVQA) on 360° video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360° video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360° video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360° video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360° video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360° video over two datasets, the code of which has been public in https://github.com/yanglixiaoshen/ProVQA.

A Super-Resolution-Based Feature Map Compression for Machine-Oriented Video Coding

Recently, video and image compression methods using neural networks have received much attention. In MPEG standardization, Video Coding for Machine (VCM) is a newly arising topic which attempts to compress features/images for the purpose of machine vision tasks. Especially, compressing features has advantages in terms of privacy protection and computation off-loading. In this paper, we propose an effective feature compression method equipped with a super-resolution (SR) module for features. Our main motivation comes from the observation that features are somewhat robust to spatial distortions (e.g., AWGN, blur, quantization distortions, coding artifacts), which leads us to integrating an SR module into the compression framework. We also further explore the best training strategy of the proposed method, i.e., finding the best combination of various losses and proper input feature shapes. Our comprehensive experiments show that the proposed method outperforms the baseline in the original VCM anchor scenario on various QP values with Versatile Video Coding (VVC). Specifically, the proposed framework achieved up to 50% BD-rate reduction compared to the conventional P-layer feature map compression method for the object detection task on the OpenImage dataset.

Geometric Distortion Correction of Images Received From Biometric Fingerprint Devices

The quality of fingerprint images is very important for recognition and identification in biometric systems. Geometric distortions and different image scales may worsen fingerprint recognition. It is necessary to adjust distortions and scale in biometric finger capture systems. Algorithms for correcting distortions and scale are investigated, analyzed, and developed in this paper. A polynomial model of second order is used as a mathematical model of spatial distortions. To correct the brightness of image points, bicubic interpolation is used.

Compensation method for spatial phase distortion based on autocorrelation in laser heterodyne detection

The signal-to-noise ratio (SNR) of heterodyne detection is seriously reduced by the spatial phase distortion, so the compensation method of the phase distortion is of great significance for improving the performance of the heterodyne system. By replacing the single detector with an array detector in the system, the previous compensation method based on sequence shift and optimization algorithm has a certain effect. However, the method also has various shortcomings, such as long processing time, poor search stability, and possible false alarm, which severely limits its practical application. To solve the above-mentioned problems, the autocorrelation operation is used to achieve the equiphase superposition of the signals output by the elements of the array detector, thereby realizing the phase compensation. In comparison to the previous method, our method does not need an optimization algorithm, which avoids the long iterative operations and considerably improves the processing efficiency. Besides, this method avoids the false alarm caused by the optimization algorithm. The numerical calculation indicates that in case of severe phase distortion, the proposed method can increase the SNR by dozens of dB compared with the single-point detector system, thus proving the effectiveness of this method. The study results may provide a feasible and effective phase compensation method for improving and promoting the laser heterodyne detection performance.

MPFINet: A Multilevel Parallel Feature Injection Network for Panchromatic and Multispectral Image Fusion

The fusion of a high-spatial-resolution panchromatic (PAN) image and a corresponding low-resolution multispectral (MS) image can yield a high-resolution multispectral (HRMS) image, which is also known as pansharpening. Most previous methods based on convolutional neural networks (CNNs) have achieved remarkable results. However, information of different scales has not been fully mined and utilized, and still produces spectral and spatial distortion. In this work, we propose a multilevel parallel feature injection network that contains three scale levels and two parallel branches. In the feature extraction branch, a multi-scale perception dynamic convolution dense block is proposed to adaptively extract the spatial and spectral information. Then, the sufficient multilevel features are injected into the image reconstruction branch, and an attention fusion module based on the spectral dimension is designed in order to fuse shallow contextual features and deep semantic features. In the image reconstruction branch, cascaded transformer blocks are employed to capture the similarities among the spectral bands of the MS image. Extensive experiments are conducted on the QuickBird and WorldView-3 datasets to demonstrate that MPFINet achieves significant improvement over several state-of-the-art methods on both spatial and spectral quality assessments.

Numerical simulation of pipeline crack detection probe with poly-magnetic structure

The detection and characterization of cracks is one of the key issues in oil and gas transmission pipeline inspection. The weak degree of spatial magnetic field distortion caused by the small size of the crack creates great difficulties for signal extraction. In this paper, we propose an ACM crack detection probe with poly-magnetic structure, and investigate the influence of the core, shield on the detection signal through numerical simulations. The results show that the crack detection probe with the core and shield can effectively improve the distorted magnetic field component in space acquired by the magnetic element, increasing the distorted magnetic field signals in the vertical and horizontal directions by a factor of 2.10 and 1.76 respectively. Verification that ACM probe with poly-magnetic structure can effectively increases the crack-induced magnetic field distortion signal and reduce the probability of misses.

Physics of a diffraction-quality optical system, taking into account the range of received radiation

The process of detecting, selecting and measuring the parameters of the movement of the observation object has a pronounced probabilistic nature. This is caused by source emission fluctuations, interference from extended and point natural sources distributed over the field of view at the input of the opto-electronic device and its own noise. As a result, the output information of the optoelectronic device, after processing by the onboard system, is output to the control and navigation system with a certain error that determines the quality of the aircraft’s control and navigation system information. The resolution is estimated depending on the equivalent weight function of the optical system and intermediate links which characterizes spatial distortions introduced into the optical image of an object taking into account the range of radiation received by the matrix photodetector. We obtained an expression that allows us to estimate the potential resolving power of the optoelectronic device taking into account the finite wavelength range of the received radiation and determine the condition under which the receiver can be considered to be spatially continuous. The present paper is devoted to estimation of the resolving power of an optoelectronic device with a matrix photodetector of radiation.

Integrating multi-criteria analysis and spherical cellular automata approach for modelling global urban land-use change

Existing geosimulation land-use change models are predominantly designed to operate at local or regional spatial scales. When these models are applied on data at the global level, they do not consider the effects of spatial distortions caused by the curvature of the Earth’s surface and often lack some refinements related to land suitability analysis. Therefore, the main objective of this study is to integrate multi-criteria evaluation with spherical cellular automata, to develop a novel modelling approach for simulating global urban land-use change. The world region is clustered into sub-regions to improve the suitability analysis. The obtained results reveal differences in urban growth rate and the size of the extent across the four clusters. The 64% of the total global urbanization are occurring in urban region clusters characterized by high gross domestic product and population density, while urban regions in isolated locations have the lowest urban growth rate.

Optical and SAR image fusion method with coupling gain injection and guided filtering

Significant radiometric differences and weak grayscale correlations exist between optical and SAR images. As a result, there are severe spectral and spatial distortions in the fused images. We propose a fusion method of optical and SAR remote sensing images that couples the gain injection method and the guided filter. The proposed method is based on the fusion framework of generalized intensity-hue-saturation non-subsampled contourlet transform, and the gain injection is used for the low-frequency coefficient fusion to reduce the spectral distortion. Then, the divergence is used as the activity measure operator to calculate the initial weight template for the high-frequency coefficients. The guided filter is used to optimize the edge details of the initial weight template. The fused high-frequency coefficients are obtained by weighted average. Through comparison experiments with existing fusion methods, the results show that the proposed method has the best quality of fusion and the proposed method has the best performance.

Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening

Dear Editor, In pansharpening task, the most existing deep-learning-based pan-sharpening methods fail to fully utilize the different level features, inevitably leading to spectral or spatial distortions. To address this challenge, in this letter, we propose a dual-branch multi-level feature aggregation network for pansharpening (DMFANet). The experimental results on the WorldView-II (WV-II) and QuickBird (QB) dataset confirmed the notable superiority of our method over the current state-of-the-art methods from quantitative and qualitative point of view. The source code is available at https://github.com/Gui-Cheng/DMFANet.