Fuzzy Regression for Perceptual Image Quality Assessment

Subjective image quality assessment (IQA) is fundamentally important in various image processing applications such as image/video compression and image reconstruction, since it directly indicates the actual human perception of an image. However, fuzziness due to human judgment is neglected in current methodologies for predicting subjective IQA, where the fuzziness indicates assessment uncertainty. In this article, we propose a fuzzy regression method that accounts for fuzziness introduced through human judgment and the limitations of widely-used psychometric quality scales. We demonstrate how fuzzy regression models provide fuzziness information regarding subjective IQA. We benchmark the fuzzy regression method against the commonly used explicit modeling method for subjective IQA namely statistical regression by considering three real situations involving subjective image quality experiments where: (a) the number of participants is insufficient; (b) an insufficient amount of data is used for modelling; and (c) variant fuzziness is caused by human judgment. Results indicate that fuzzy regression models achieve more effective data fitting and better generalization capability when predicting subjective IQA under different types and levels of image distortion

Varying Spread Fuzzy Regression for Affective Quality Estimation

Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state of art and the recently developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria, which address both predictions of magnitudes and fuzziness

An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired persons

Smartphone applications based on object detection techniques have recently been proposed to assist visually impaired persons with navigating indoor environments. In the smartphone, digital cameras are installed to detect objects which are important for navigation. Prior to detect the interested objects from images, edges on the objects have to be identified. Object edges are difficult to be detected accurately as the image is contaminated by strong image blur which is caused by camera movement. Although deblurring algorithms can be used to filter blur noise, they are computationally expensive and not suitable for real-time implementation. Also edge detection algorithms are mostly developed for stationary images without serious blur. In this paper, a modified sigmoid function (MSF) framework based on inertial measurement unit (IMU) is proposed to mitigate these problems. The IMU estimates blur levels to adapt the MSF which is computationally simple. When the camera is moving, the topological structure of the MSF is estimated continuously in order to improve effectiveness of edge detections. The performance of the MSF framework is evaluated by detecting object edges on video sequences associated with IMU data. The MSF framework is benchmarked against existing edge detection techniques and results show that it can obtain comparably lower errors. It is further shown that the computation time is significantly decreased compared to using techniques that deploy deblurring algorithms, thus making our proposed technique a strong candidate for reliable real-time navigation

Deblurring Filter Design Based on Fuzzy Regression Modeling and Perceptual Image Quality Assessment

Images captured by digital cameras are generally not perfect as image blurring is usually generated by camera motion through long hand-held exposure. Deblurring filters can be used to improve image quality by removing image blur. Prior to develop a deblurring filter, a simulator for image quality assessment is essential to optimize filter parameters. Although subjective image quality assessment (subjective IQA) is commonly used for evaluating the visual effect of digital images for a wide range of image processing applications, it is inconvenient to be implemented in real-time. Generally, statistical regression is used to generate a functional map to correlate the subjective IQA and the objective image quality metrics. However, it cannot address the uncertainty caused by human judgment during the subjective IQA. This paper first proposes a fuzzy regression method to develop the functional map that overcomes the limitation of statistical regression that cannot account for uncertainty introduced through human judgment. Based on the fuzzy regression models, the deblurring filter parameters can be optimized. Experimental results show that the satisfactory deblurring can be achieved on blurred images captured by a smartphone camera

Structural and functional analysis of ypt2, an essential ras-related gene in the fission yeast Schizosaccharomyces pombe encoding a Sec4 protein homologue.

Using the cloned Saccharomyces cerevisiae YPT1 gene as hybridization probe, a gene, designated ypt2, was isolated from the fission yeast Schizosaccharomyces pombe and found to encode a 200 amino acid long protein most closely related to the ypt branch of the ras superfamily. Disruption of the ypt2 gene is lethal. The bacterially produced ypt2 gene product is shown to bind GTP. A region of the ypt2 protein corresponding to but different from the 'effector region' of ras proteins is also different from that of ypt1 proteins of different species but identical to the 'effector loop' of the S.cerevisiae SEC4 gene product, a protein known to be required for vesicular protein transport. The S.pombe ypt2 gene under control of the S.cerevisiae GAL10 promoter is able to suppress the temperature-sensitive phenotype of a S. cerevisiae sec4 mutant, indicating a functional similarity of these GTP-binding proteins from the two very distantly related yeasts

A reduced-reference perceptual image and video quality metric based on edge preservation

In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al

Intra-oral compartment pressures: a biofunctional model and experimental measurements under different conditions of posture

Oral posture is considered to have a major influence on the development and reoccurrence of malocclusion. A biofunctional model was tested with the null hypotheses that (1) there are no significant differences between pressures during different oral functions and (2) between pressure measurements in different oral compartments in order to substantiate various postural conditions at rest by intra-oral pressure dynamics. Atmospheric pressure monitoring was simultaneously carried out with a digital manometer in the vestibular inter-occlusal space (IOS) and at the palatal vault (sub-palatal space, SPS). Twenty subjects with normal occlusion were evaluated during the open-mouth condition (OC), gently closed lips (semi-open compartment condition, SC), with closed compartments after the generation of a negative pressure (CCN) and swallowing (SW). Pressure curve characteristics were compared between the different measurement phases (OC, SC, CCN, SW) as well as between the two compartments (IOS, SPS) using analysis of variance and Wilcoxon matched-pairs tests adopting a significance level of α = 0.05. Both null hypotheses were rejected. Average pressures (IOS, SPS) in the experimental phases were 0.0, −0.08 (OC); −0.16, −1.0 (SC); −48.79, −81.86 (CCN); and −29.25, −62.51 (SW) mbar. CCN plateau and peak characteristics significantly differed between the two compartments SPS and IOS. These results indicate the formation of two different intra-oral functional anatomical compartments which provide a deeper understanding of orofacial biofunctions and explain previous observations of negative intra-oral pressures at rest