B. Ortiz-Jaramillo

Evaluating color difference measures in images

The most well known and widely used method for comparing two homogeneous color samples is the CIEDE2000 color difference formula because of its strong agreement with human perception. However, the formula is unreliable when applied over images and its spatial extensions have shown little improvement compared with the original formula. Hence, researchers have proposed many methods intending to measure color differences (CDs) in natural scene color images. However, these existing methods have not yet been rigorously compared. Therefore, in this work we review and evaluate CD measures with the purpose of answering the question to what extent do state-of-the-art CD measures agree with human perception of CDs in images? To answer the question, we have reviewed and evaluated eight state-of-the-art CD measures on a public image quality database. We found that the CIEDE2000, its spatial extension and the just noticeable CD measure perform well in computing CDs in images distorted by black level shift and color quantization algorithms (correlation higher than 0.8). However, none of the tested CD measures perform well on identifying CDs for the variety of color related distortions tested in this work, e.g., most of the tested CD measures showed a correlation lower than 0.65.

Content-aware objective video quality assessment

Abstract. Since the end-user of video-based systems is often a human observer, prediction of user-perceived video quality (PVQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures (VQMs), their lack of generalizability remains a problem. This is mainly due to the strong dependency between PVQ and video content. Although this problem is well known, few existing VQMs directly account for the influence of video content on PVQ. Recently, we proposed a method to predict PVQ by introducing relevant video content features in the computation of video distortion measures. The method is based on analyzing the level of spatiotemporal activity in the video and using those as parameters of the anthropomorphic video distortion models. We focus on the experimental evaluation of the proposed methodology based on a total of five public databases, four different objective VQMs, and 105 content related indexes. Additionally, relying on the proposed method, we introduce an approach for selecting the levels of video distortions for the purpose of subjective quality assessment studies. Our results suggest that when adequately combined with content related indexes, even very simple distortion measures (e.g., peak signal to noise ratio) are able to achieve high performance, i.e., high correlation between the VQM and the PVQ. In particular, we have found that by incorporating video content features, it is possible to increase the performance of the VQM by up to 20% relative to its noncontent-aware baseline.

Computing contrast ratio in images using local content information

It is well know that a measure of contrast in images is not yet fully defined. The conventional measures of contrast consist of global computations and therefore they have a poor performance. At the same time image quality assessment is often based on quantifying the visibility between a structure of interest or foreground and its surrounding background, i.e., the contrast ratio. Then, a high quality image is the one in which structures of interest are well distinguishable from the background. Therefore, the computation of contrast ratio is important in automatic image quality assessment and it should be computed locally taking into account the local distribution of pixel values. We estimate the contrast ratio by using Weber contrast in local image patches. The main contribution of this work lies in the characterization of local distribution of pixel values which is used for computing the contrast ratio. Here, local image patches are characterized by bimodal histograms representing a set of pixels which are likely to be inside the foreground and another set likely to be in the background. The local contrast ratio is estimated using the ratio between mean intensity values of each mode of the histogram. Our experimental results over two public image databases show that the proposed method is able to accurately predict changes of quality due to contrast decrements (Pearson correlations higher than 90%).

Multi-resolution analysis for region of interest extraction in thermographic, nondestructive evaluation

Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection. It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest (ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image. In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian scale analysis and local edge detection. In this methodology local correlation between image and Gaussian window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size. Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the other dedicate algorithms proposed in the state of art.

Improving textures discrimination in the local binary patterns technique by using symmetry & group theory

The underlying working mechanism of Local Binary Pattern (LBP) techniques is still a topic to investigate. In this paper we explore symmetry & group theory for grouping functions that represent binary intensity changes obtained with the LBP technique. This additionally offers a strong mathematical foundation for the basis of the technique. We include complement and mirror invariants to the known LBP rotational invariant. We tested our algorithm using 13 textures from the Brodatz database. The statistical analysis shows that combining rotational, mirrored and complemented versions of local texture results in an improvement in the performance of the technique in terms of accuracy describing textures and discrimination distinguishing textures.

Quantifying appearance retention in carpets using geometrical local binary patterns

Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations have shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique is better for distinguishing consecutive AR grades in carpets.

Optimizing feature extraction in image analysis using experimented designs: a case study evaluating texture algorithms for describing appearance retention in carpets

When performing image analysis, one of the most critical steps is the selection of appropriate techniques. A huge amount of features can be extracted from several techniques and the selection is commonly performed based on expert knowledge. In this paper we present the theory of experimental designs as a tool for an objective selection of techniques in image analysis domain. We present a study case for evaluating appearance retention in textile floor coverings using texture features. The use of experimental design theory permitted to select an optimal set of techniques for describing the texture changes due to degradation.

iFAS: Image Fidelity Assessment

image Fidelity Assessment (iFAS) is a software tool designed to assist image quality researchers providing easy access to a range of state-of-the-art measures which can be applied on a single pair of images and/or in a full database, as well as intuitive visualizations that aid data analysis, e.g., images and histograms of pixel-wise image differences, scatter plots and correlation analysis. The software is freely available for non-commercial use.

Content-aware video quality assessment: predicting human perception of quality using peak signal to noise ratio and spatial/temporal activity

Since the end-user of video-based systems is often a human observer, prediction of human perception of quality (HPoQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures, one problem is the lack of generalizability. This is mainly due to the strong dependency between HPoQ and video content. Although this problem is well-known, few existing methods directly account for the influence of video content on HPoQ. This paper propose a new method to predict HPoQ by using simple distortion measures and introducing video content features in their computation. Our methodology is based on analyzing the level of spatio-temporal activity and combining HPoQ content related parameters with simple distortion measures. Our results show that even very simple distortion measures such as PSNR and simple spatio-temporal activity measures lead to good results. Results over four different public video quality databases show that the proposed methodology, while faster and simpler, is competitive with current state-of-the-art methods, i.e., correlations between objective and subjective assessment higher than 80% and it is only two times slower than PSNR.

Selection of optimal texture algorithms for evaluating degradation of carpets through experimental design

Abstract. Optimal texture analysis algorithms for describing degradation of carpets are identified. Experimental design is applied to select from a set of texture analysis algorithms those optimal for identifying texture changes due to degradation of carpets. The degree of wear of a degraded carpet is quantified by comparing its texture to the original texture. The set of texture algorithms is applied on intensity images obtained from the American and the European standards. The performance of the texture algorithms is evaluated using measures that quantify characteristics in the relationship between the metrics and the changes in texture. The statistical analysis of the experimental results shows that the local binary patterns algorithm is optimal in >50% of the cases, for describing degradation of the carpets. Other texture algorithms that optimally characterize the degradation of carpets include the use of the power spectrum, Wigner distribution, and average co-occurrence matrix algorithms.