Quantifying image distortion based on Gabor filter bank and multiple regression analysis

Image quality assessment is indispensable for image-based applications. The approaches towards image quality assessment fall into two main categories: subjective and objective methods. Subjective assessment has been widely used. However, careful subjective assessments are experimentally difficult and lengthy, and the results obtained may vary depending on the test conditions. On the other hand, objective image quality assessment would not only alleviate the difficulties described above but would also help to expand the application field. Therefore, several works have been developed for quantifying the distortion presented on a image achieving goodness of fit between subjective and objective scores up to 92%. Nevertheless, current methodologies are designed assuming that the nature of the distortion is known. Generally, this is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. Therefore, we believe that the current methods of image quality assessment should be adapted in order to identify and quantify the distortion of images at the same time. That combination can improve processes such as enhancement, restoration, compression, transmission, among others. We present an approach based on the power of the experimental design and the joint localization of the Gabor filters for studying the influence of the spatial/frequencies on image quality assessment. Therefore, we achieve a correct identification and quantification of the distortion affecting images. This method provides accurate scores and differentiability between distortions

No-reference Image Denoising Quality Assessment

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a no-reference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and denoising models alone cannot robustly rank denoising results, they often complement each other. We accordingly design denoising quality features based on these existing metrics and models and then use Random Forests Regression to aggregate them into a more powerful unified metric. Our experiments on images with various types and levels of noise show that our no-reference denoising quality assessment method significantly outperforms the state-of-the-art quality metrics. This paper also provides a method that leverages our quality assessment method to automatically tune the parameter settings of a denoising algorithm for an input noisy image to produce an optimal denoising result.Comment: 17 pages, 41 figures, accepted by Computer Vision Conference (CVC) 201

Biometric Security System for Fake Detection using image Quality Assessment Techniques

Biometric Identification is the best security system in developing security world. Hackers made a fake biometric in easy way, so it reduce the security accuracy. Biometric security system are hacked by using two types of attack, direct or spoofing attack & indirect attack. So, there is a need to develop a new and efficient protection measure. Here, three biometric techniques - face recognition, fingerprint and iris recognition (Multi-biometric system) are used to detect whether the image is real or fake. This protection method uses Image Quality Assessment (IQA) technique. If the image is a real, it checks whether the image belongs to an authorized person or not. Out of the three biometric techniques, face recognition using Discriminate Power Analysis (DPA) technique is used for detecting authorized person. The objective is to increase the security of biometric recognition framework by adding liveness assessment in fast, non-invasive & user friendly manner. This approach protects the biometric security system from direct and indirect attack. The complexity of the proposed system is very low because it extracts 31 image quality measures from one image

Image Quality Measures for Gender Classification

The major problem that we are facing in biometric systems is the use of fake biometric identifiers. Fake biometric identifiers can be of the form where one person imitates as another by falsifying data and thereby gaining an illegitimate advantage. This can be achieved either by using fake self manufactured synthetic or reconstructed samples. Gender classification has become an essential part in most human computer interactions especially in high security areas where gender restrictions are provided. In this paper, software based multi-biometric system that is used to classify real and fake face samples and a gender classification are presented. The main objective of the paper is to improve biometric detection in a fast, non intrusive way which maintains the generality that is lacking in other anti-spoofing methods. The proposed method incorporates liveness detection, extracts 25 general image quality measures from the input image and then classifies the input into real or fake sample. Algorithm for Gender classification is developed in accordance with the facial features. There features are classified into two i) appearance based ii) Geometric based. The image quality assessment algorithm is developed and tested with ATVS database. The gender classification with image quality assessment is developed and tested with medical students database. DOI: 10.17762/ijritcc2321-8169.15017

Investigation of the effectiveness of video quality metrics in video pre-processing.

This paper presents an investigation of the effectiveness of current video quality measurement metrics in measuring variations in perceptual video quality of pre-processed video. The results show that full-reference video quality metrics are not effective in detecting variations in perceptual video quality. However, no reference metrics show better performance when compared to full reference metrics, particularly, Naturalness Image Quality Evaluator (NIQE) is notably better at detecting perceptual quality variations