Luminance Distortion Research Articles

81‐4: Viewing Angle‐aware Color and Luminance Distortion Compensation for Automotive OLED Displays

Recently, Organic Light Emitting Diode (OLED) displays have been widely adopted in vehicles. However, OLED displays face a challenge in that color and luminance become distorted significantly depending on the viewing angle. This issue is particularly crucial for the Center Information Display (CID) installed to the right of the driver, which is always viewed at an angle. To address this problem, this paper proposes a software‐based method to compensate for the color and luminance distortions caused by viewing angles in vehicle OLED displays. This method involves analyzing the degree of distortion at different angles and applying corrections accordingly. We have validated the effectiveness of this approach through a series of experiments.

Evaluation of the results of pansharpening multispectral images

When processing digital images obtained by remote sensing of the Earth, various methods are used to increase their resolution. However, in this case, some distortions of a different nature may appear on the images. For example, luminance distortion (color, contrast, sharpness) and geometric (object boundary deformations). Developers of automated image processing systems face the task of choosing from dozens of methods the one that introduces the least visually noticeable distortions, i.e. creates images of the best quality.In this article, the following problem was solved: to determine the functions for assessing the quality of images formed as a result of multispectral satellite image pansharpening. The pansharped image cannot be compared with the template one, since it does not exist. To assess quality of such images, we proposed to use the so-called no-reference evaluation measures.The article briefly describes methods for synthesizing a new high-resolution color image from four images of Earth remote sensing. Functions for calculating quantitative estimates of the quality of the resulting images are discussed. Results of some space image pansharpening by different methods are presented. Graphs of these assessments of image quality are constructed. To evaluate panchromatic fusion results, the following non-reference quality scores are recommended: FISH, LOCC, LOEN, NATU, SHAR, and WAVS. The clearest boundaries and natural colors of objects were demonstrated by the P+XS pansharpening algorithm based on a linear combination of spectral channels.

Perceptual image quality assessment based on gradient similarity and Ruderman operator

In this work, a new metric for image quality assessment is suggested, which provides more suppleness than previous measures in using Ruderman operator, visual region of interest and gradient similarity. Firstly, the luminance distortion between the reference and test images is determined. Secondly, the gradient similarity is computed by using canny filter and proposed gradient mask. Thirdly, the test and reference images are transformed using Ruderman operator. Fourthly, the visual region of interest is calculated by employing entropy operator. Lastly, the dissimilarity between the reference and test images is obtained, by combining all previous metrics: luminance distortion measure, gradient similarity measures, Ruderman measure and visual region of interest measure. Experimental comparison demonstrates the effectiveness of the proposed method.

SBD-Duo: a dual stage shot boundary detection technique robust to motion and illumination effect

In this paper, we propose a novel shot boundary detection technique using gradient and colour information. The gradient similarity and luminance distortion are calculated to measure the contrast and structural changes of each frame including luminance changes. In the proposed system, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to extract the possible transitions using an adaptive threshold across the videos. In the verification part, CIEDE2000 colour-difference values of the possible transition frames are compared for declaration of abrupt and gradual transitions. Our system takes effectively less computational time to detect abrupt and gradual transition for a video as compared with contemporary solutions. Our proposed system also gives dominate the performance as compared with latest techniques in terms of F1 score using TRECVid 2001 and 2007 selected dataset. We have performed a series of rigorous experimentation to validate our claims.

Optimised enhancement scheme for low contrast underwater images

Images acquired in underwater environments are usually affected by light absorption and scattering. These are the two phenomena that reduce the clarity of images that are captured in these environments. These factors cause low contrast and anamorphic colour diffusion. To tackle these issues, we propose an optimized low contrast enhancement scheme. The main thrust of this paper borders on enhancement of underwater image contrast by preserving the brightness level. The approach is termed Fuzzy-Histogram Equalisation Optimised for Brightness Preservation (FHEOBP) technique, where a combination of fuzzy and classical histogram equalisation techniques is employed towards the enhancement of the contrast of images from underwater scene. The scheme is optimized using teaching-learning-based optimisation technique that is built into the algorithm. The proposed FHEOBP filter shows improved performance over Local Histogram Equalisation (LHE) and Global Histogram Equalisation (GHE) as it has a higher luminance distortion index value than those of LHE and GHE. This translates into a better image details preservation. In fact, the computed luminance distortion indices for optimised FHEOBP are 16.4%, 28.3% and 20.1%, respectively higher than those of the corresponding GHE, in the same test images utilised for performance evaluation. Between the optimised and non-optimised FHEOBP, luminance distortion figures of optimised FHEOBP are 8%, 6.8% and 9.8% higher than those of the equivalent non-optimised FHEOBP in the test image data set.

28‐2: A Novel Local Dimming algorithm with HDR for VR system based on GPU

As the screen of VR is very close to human eye and the optical lens has amplification effect, VR needs high PPI to meet people's visual requirements. High PPI panel with side‐emitting LED backlight is difficult to realize high contrast ratio(CR). Local Dimming based on direct‐illumination LED can not only save backlight power consumption but also improve the CR of the VR system. The point spread function(PSF) of LED plays an important role in Local Dimming technology. However traditional PSF always adopts approximate calculation method with low accuracy, which causes luminance distortion. In this paper, a method of PSF measurement and data processing was proposed. It can describe the spread strength and range of one single block LED actually. One improved LCD compensation algorithm was demonstrated in this paper. The light leakage characteristic of liquid crystal was considered. It can keep the hue consistent. The proposed Local Dimming method has low distortion and high dynamic contrast range. This paper also presents a method for implementing Local Dimming algorithm by GPU, which can display Local Dimming effect in real time of VR system with high frame rate.

Application of rank metric codes in digital image watermarking

In this paper, we propose a new digital image watermarking algorithm where the resistance against attacks is studied using error correcting codes. Using the well known Lattice QIM in the spatial domain, we propose to use a different kind of error correcting codes called rank metric codes. These codes are already well used in cryptography and communications for network coding but not used yet in the context of watermarking.In this article, we show how this metric permits to correct errors with a specific structure and is adapted to specific image attacks when combined with a watermarking technique. In particular, we describe a rank metric code family called Gabidulin codes analogous to the well known Reed–Solomon codes. If one considers a rank code over a finite field extension, then any codeword has a matrix representation. One can decode the original message if the matrix rank of the detected codeword is small enough.We propose a study to validate the concept of rank metric in watermarking applications. First, we introduce a theoretically invariant method to luminance additive constant change. After combining the Lattice QIM method and rank metric codes, we add a multi-detection strategy on the damaged images with controlled luminance distortions. Then, using a block-based watermarking approach, we show how the proposed association can also be robust to an image distortion we called content erasure or copy-paste. The proposed approach completes other watermarking strategies against attacks with random errors such as JPEG compression.

Qualitative assessment of geostatistical and non-geostatistical fusion techniques: a case study on landsat 8 images

Broadly satellite images are available in two categories (1) high spectral resolution but less spatial resolution (2) high spatial resolution but less spectral resolution. But in certain applications, images with high spatial as well as high spectral resolution are required. To meet such kind of requirement, Image fusion is widely accepted and increasingly being used. In this study satellite image fusion is done using geostatistical methods (cokriging, regression kriging) and non-geostatistical methods (intensity hue saturation, principal component analysis). The study is focused on performing qualitative assessment of selected image fusion techniques. In this study, the primary variable is RGB bands of Landsat 8 Operational Land Imager (OLI) and the panchromatic band is chosen as the second variable. The output of these selected methods is compared to access spectral and spatial quality. Spectral quality is accessed by finding the correlation between the primary variable and the output, however spatial quality is accessed via texture analysis method named entropy. Overall assessment of loss of correlation, luminance distortion, and contrast distortion is done using Image quality index. Correlation index of regression kriging and PCA are comparable whereas entropy and image quality index of fused output is highest in case of regression kriging. Hence regression kriging can be concluded as the best fusion technique out of the compared techniques.

Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.

Detection of chromatic and luminance distortions in natural scenes.

A number of studies have measured visual thresholds for detecting spatial distortions applied to images of natural scenes. In one study, Bex [J. Vis.10(2), 1 (2010)10.1167/10.2.231534-7362] measured sensitivity to sinusoidal spatial modulations of image scale. Here, we measure sensitivity to sinusoidal scale distortions applied to the chromatic, luminance, or both layers of natural scene images. We first established that sensitivity does not depend on whether the undistorted comparison image was of the same or of a different scene. Next, we found that, when the luminance but not chromatic layer was distorted, performance was the same regardless of whether the chromatic layer was present, absent, or phase-scrambled; in other words, the chromatic layer, in whatever form, did not affect sensitivity to the luminance layer distortion. However, when the chromatic layer was distorted, sensitivity was higher when the luminance layer was intact compared to when absent or phase-scrambled. These detection threshold results complement the appearance of periodic distortions of the image scale: when the luminance layer is distorted visibly, the scene appears distorted, but when the chromatic layer is distorted visibly, there is little apparent scene distortion. We conclude that (a)observers have a built-in sense of how a normal image of a natural scene should appear, and (b)the detection of distortion in, as well as the apparent distortion of, natural scene images is mediated predominantly by the luminance layer and not chromatic layer.

基于特征点分布特性的激光干扰效果评估算法

Laser active imaging systems are usually used in region surveillance and target identification. However, the photoelectric imaging detector in the imaging systems is easy to be disturbed and this leads to errors of the recognition and even the missing of the target. Assessment of laser-dazzling effects in view of the invalidation of the target feature must be better understood. A new feature-point similarity (FPSIM) assessment algorithm is proposed. The feature accelerated segmentation testing (FAST) algorithm is used to extract feature points of the original image and disturbed image. The target area is obtained via feature-point matching, and the feature-point maintenance as well as stabilization is computed in the target area. The location of the feature points in the original image is obtained, and the local luminance and contrast distortion are compared in the same place of the two images. The normalized FPSIM is obtained via product of the feature-point maintenance, stabilization, luminance distortion and contrast distortion. The luminance imaging experiment is performed for the target by utilizing the laser active imaging system. In the experiment, the disturbed images of different disturbing powers, different intense backgrounds and different spot positions are obtained. The proposed FPSIM algorithm is used to evaluate the newly obtained laser-dazzling images, and the results show that the FPSIM reflects the varieties of the feature points in target recognition objectively. Compared with normalized mean square error (NMSE) and structural simila rity (SSIM), the FPSIM gives a more reasonable evaluation result for different laser-dazzling images. The evaluation results are more suitable for the subjective visual feeling, and FPSIM can also give the guidance of the laser active imaging system defense and application.

Optimized Hybrid Security Mechanism for Image Authentication and Secrecy using PSO

Authentication is one of the image security issues solved by hash function and another one issue is providing security for illegal manipulation of digital image is solved by an encryption. An optimized hybrid image security mechanism for authentication and secrecy of images by means of Particle Swarm Optimization (PSO) in daubechies4 transform is illustrated in this paper. This mechanism provide solutions to the issues such as authentication, robustness, security and statistical attacks.The PSO technique is employed to select feature vectors to form the image hash and select high energy coefficients for partial encryption. The shuffling of bits , coefficients and blocks of an image is performed by interweaving technique. The Completeness of Signature (CoS) is used torecognize the imageas authentic or unauthentic. The image quality distortion is computed with help of image quality index metric(IQIM) with respect to three factors namelyloss of correlation, luminance distortion, and contrast distortion. The experimental results are computed with respect to CoS, IQIM, PSNR and correlation coefficient and presented to demonstrate the efficacy of the proposed scheme.

Luma-Chroma Space Filter Design for Subpixel-Based Monochrome Image Downsampling

In general, subpixel-based downsampling can achieve higher apparent resolution of the down-sampled images on LCD or OLED displays than pixel-based downsampling. With the frequency domain analysis of subpixel-based downsampling, we discover special characteristics of the luma-chroma color transform choice for monochrome images. With these, we model the anti-aliasing filter design for subpixel-based monochrome image downsampling as a human visual system-based optimization problem with a two-term cost function and obtain a closed-form solution. One cost term measures the luminance distortion and the other term measures the chrominance aliasing in our chosen luma-chroma space. Simulation results suggest that the proposed method can achieve sharper down-sampled gray/font images compared with conventional pixel and subpixel-based methods, without noticeable color fringing artifacts.

A Wavelet-based Algorithm for Vehicle Flow Information Extraction

This paper proposed an improved algorithm applied in video intelligent traffic control system for vehicle detection . The accuracy of original algorithm, which is based on the comparision of contrast and luminance distortion of present image with background, reduces greatly under bad weather because of false detection caused by noises in captured images. In this paper we chose Daubechies wavelet as mother wavelet to add a 2-dimension wavelet process before the algorithm , just after the image is captured, to de-noise each captured image. We used FPGA-based equipments to test the algorithm , and the experiment proved higher performance of improved algorithm, especially under bad weather. http://dx.doi.org/10.11591/telkomnika.v11i1.1916

Optimized Image Watermarking Scheme Based On PSO

The digital watermarking scheme provides copyright protection to multimedia contents especially to digital images. In this paper a secure optimized watermarking scheme for copyright protection of images using Particle Swarm Optimization(PSO) in daubechies4 transform is proposed.. The image quality index metrics (IQIM) is used to measure the image quality distortion which is modeled with respect to factors such as loss of correlation, luminance distortion, and contrast distortion. The coefficients are selected in Daubechies domain with help of particle swarm optimization to embed the DCT transformed selected high energy coefficients watermark bits in the host image. Different attacks are employed in PSO training to assess the robustness and imperceptibility of watermarked image. The experimental results are presented to demonstrate the effectiveness of the proposed scheme.

Automatic Detection of Defects on Periodically Patterned Textures

Defect detection is a major concern in quality control of various products in industries. This paper presents two different machine-vision based methods for detecting defects on periodically patterned textures. In the first method, input defective image is split into several blocks of size same as the size of the periodic unit of the image and chi-square histogram distances of each periodic block with respect to itself and all other periodic blocks are calculated to get a dissimilarity matrix. This dissimilarity matrix is subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. The second method of defect detection is based on Universal Quality Index which is a measure of loss of correlation, luminance distortion and contrast distortion between any two signals. Quality indices of a periodic block with respect to itself and all other periodic blocks are calculated to get a similarity matrix containing quality indices. Specific variances of the periodic blocks are derived from the quality index matrix through orthogonal factor model based on eigen decomposition. These variances are subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results of experiments on real fabric images with defects show that the defect detection methods based on chi-square histogram distance and universal quality index yield a success rate of 98.6% and 97.8% respectively.

Short-term memory of color sensation is robust against luminance distortion

It is well-known that past knowledge affects color perception, as for example in the perception of familiar objects having richer colors. Although there have been many studies on human memory function with regard to color perception, it is not clear how color signals are treated in the early stages of human memory formation. Here, we tested whether in early memory color information is conserved without any interaction of other visual information. We found that performance of chromatic discrimination between a memorized color and a test color did not deteriorate under any contrast condition of dynamic luminance masking, and thus concluded that color signals were not disturbed by dynamic luminance signals. The results indicate that chromatic sensation per se is not modified at the early stages of the human memory system. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011

Systematic image quality assessment for sewer inspection

Closed circuit television (CCTV) has been applied in many developing or developed counties for sewer inspection due to its low setup cost and technical requirement. Several automated diagnosis systems of sewer pipe defects had been developed to assist the technicians in interpreting or classifying sewer pipe defects. However, many researchers pointed out that good image quality is the prerequisite for accurate interpretation and diagnosis of CCTV inspection but has not a proper evaluation approach. In this paper, a CCTV image quality index considering both of the luminance distortion and the contrast distortion of a CCTV image compared by reference images is proposed and was applied to assess the image quality of the CCTV images shot for a sewer house-connection project. The experimental result indicates that rather than luminance contrast plays a more important role in the CCTV image quality that can be effectively improved by contrast enhancement. Since CCTV image quality can hardly distinguished by human eyes, the proposed image quality index can provide helpful information to efficiently assist the on-site technicians in precisely shooting better CCTV images for the pipe defection. Additionally, a sensitivity analysis of contrast stretch was implemented to quantify the CCTV image quality improvement. CCTV imaging conditions, such as pipe materials and imager status, are found as the factors affecting the CCTV image quality. In the future, a real-time CCTV image quality assessment will be developed by modifying the CCTV image quality index as an instantaneous reference for imaging adjustment that can be expected to be practicable for the on-site sewer inspection because of the extremely short computation time.

Image-Quality-Based Adaptive Face Recognition

The accuracy of automated face recognition systems is greatly affected by intraclass variations between enrollment and identification stages. In particular, changes in lighting conditions is a major contributor to these variations. Common approaches to address the effects of varying lighting conditions include preprocessing face images to normalize intraclass variations and the use of illumination invariant face descriptors. Histogram equalization is a widely used technique in face recognition to normalize variations in illumination. However, normalizing well-lit face images could lead to a decrease in recognition accuracy. The multiresolution property of wavelet transforms is used in face recognition to extract facial feature descriptors at different scales and frequencies. The high-frequency wavelet subbands have shown to provide illumination-invariant face descriptors. However, the approximation wavelet subbands have shown to be a better feature representation for well-lit face images. Fusion of match scores from low- and high-frequency-based face representations have shown to improve recognition accuracy under varying lighting conditions. However, the selection of fusion parameters for different lighting conditions remains unsolved. Motivated by these observations, this paper presents adaptive approaches to face recognition to overcome the adverse effects of varying lighting conditions. Image quality, which is measured in terms of luminance distortion in comparison to a known reference image, will be used as the base for adapting the application of global and region illumination normalization procedures. Image quality is also used to adaptively select fusion parameters for wavelet-based multistream face recognition.

Application of the central weighted structural similarity index for the estimation of the face recognition accuracy

In the paper a novel method for the estimation of the face recognition accuracy based on the modified Structural Similarity is presented. A typical application of the Structural Similarity index is related to the full-reference objective image quality assessment. Growing popularity of this metric is caused not only by the fact of its relatively low computational complexity but also by its sensitivity to three common types of distortions: the loss of contrast, luminance distortions and the loss of correlation. Taking into account the output of the SSIM metric as the quality map with the resolution nearly the same as that of the input images, it is possible to use any two-dimensional central weighting function to control the level of importance of each image region. The approach proposed in this article is based on the usage of the Central Weighted SSIM index for the prediction of the face recognition accuracy using the images contaminated by several common types of distortions e.g. salt and pepper noise, lossy compression, filtration etc. The described method is based on the assumption that facial portraits are cropped and centered, which is true for almost all biometric systems. Finally, the results of face recognition by means of PCArc method has been used, as the state-of-the art in this domain. The experiments were conducted on the Olivetti Research Lab database [1].