Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods

In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness

Application of the CIE color spaces for the digital image quality assessment

The digital image quality assessment is one of the most relevant aspects of contemporary digital image processing. A rapid development of some modern quality assessment techniques in recent years has caused the introduction of some new metrics, much better correlated with the Human Visual System (HVS) than the traditional ones such as the Mean Squared Error (MSE) or PSNR (Peak Signal to Noise Ratio). One of the most popular modern image quality assessment techniques is the usage of the Structural Similarity index (SSIM) defined in 2004. Unfortunately, even some modern image quality metrics are usually defined for the grayscale images so the colour information is often ignored. A typical classical approach to the quality assessment of the color images is the use of the Normalized Color Difference calculated in the CIE L*a*b* ob CIE Lu'v' colour space but its correlation with the human assessment is rather poor. In the paper the analysis of the influence of using the color spaces recommended by the CIE on the results of the digital image quality assessment using some modern metrics is performed. All the results have been calculated for the widely known LIVE database (release 2) containing the Differential Mean Opinion Scores (DMOS) for nearly 1000 color images with five types of distortions: JPEG compression, JPEG2000 compression, Gaussian blur, white noise and transmission over the simulated fast fading Rayleigh channel typical of wireless transmissions. As the final result the comparison of the correlations between the DMOS values and the SSIM metric calculated for various colour spaces recommended by the CIE is presented

Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods

In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness

2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy

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]

2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy

Quality Assessment of Photographed 3D Printed Flat Surfaces Using Hough Transform and Histogram Equalization

Automatic visual quality assessment of objects created using additive manufacturing processes is one of the hot topics in the Industry 4.0 era. As the 3D printing becomes more and more popular, also for everyday home use, a reliable visual quality assessment of printed surfaces attracts a great interest. One of the most obvious reasons is the possibility of saving time and filament in the case of detected low printing quality, as well as correction of some smaller imperfections during the printing process. A novel method presented in the paper can be successfully applied for the assessment of at surfaces almost independently on the filament's colour. Is utilizes the assumption about the regularity of the layers visible on the printed high quality surfaces as straight lines, which can be extracted using Hough transform. However, for various colours of filaments some preprocessing operations should be conducted to allow a proper line detection for various samples. In the proposed method the additional brightness compensation has been used together with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Results obtained for the database of 88 photos of 3D printed samples, together with their scans, are encouraging and allow a reliable quality assessment of 3D printed surfaces for various colours of filaments

Sensors in Intelligent Transportation Systems

Document type: Articl

Sensors in intelligent transportation systems

Article, editorial letter</p