176,007 research outputs found
Measurement of digital photographic image quality : survey of psychophysics just noticeable threshold difference method
Abstract: The modeling and quantification of digital photographic image quality has, from a psychophysics perspective, traditionally followed two paths, one of which is the discriminable small or just noticeable difference (local psychophysics) as detected in an image pair; further extended to cover a wide range of attribute artefactual quality variation. This method has its roots in the mathematical and psychological modeling of psychophysics and boasts a long history starting with the work of researchers such as Bernoulli, Weber and Fechner (18th, 19th century). The method models human perception of difference as a full scale logarithmic law and will be surveyed for its value in the determination of the quantitative quality of digital images
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
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Contrast sensitivity and discrimination in pictorial images
This paper describes continuing research concerned with the measurement and modeling of human spatial contrast sensitivity and discrimination functions, using complex pictorial stimuli. The relevance of such functions in image quality modeling is also reviewed. Previously1,2 we presented the choice of suitable contrast metrics, apparatus and laboratory set-up, the stimuli acquisition and manipulation, the methodology employed in the subjective tests and initial findings. Here we present our experimental paradigm, the measurement and modeling of the following visual response functions: i) Isolated Contrast Sensitivity Function (iCSF); Contextual Contrast Sensitivity Function (cCSF); Isolated Visual Perception Function (iVPF); Contextual Visual Perception Function (cVPF). Results indicate that the measured cCSFs are lower in magnitude than the iCSFs and flatter in profile. Measured iVPFs, cVPFs and cCSFs are shown to have similar profiles. Barten’s contrast detection model3 was shown to successfully predict iCSF. For a given frequency band, the reduction, or masking of cCSF compared with iCSF sensitivity is predicted from the linear amplification model (LAM)4. We also show that our extension of Barten’s contrast discrimination model1,5 is capable of describing iVPFs and cVPFs. We finally reflect on the possible implications of the measured and modeled profiles of cCSF and cVPF to image quality modeling
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
We study 3D shape modeling from a single image and make contributions to it
in three aspects. First, we present Pix3D, a large-scale benchmark of diverse
image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications
in shape-related tasks including reconstruction, retrieval, viewpoint
estimation, etc. Building such a large-scale dataset, however, is highly
challenging; existing datasets either contain only synthetic data, or lack
precise alignment between 2D images and 3D shapes, or only have a small number
of images. Second, we calibrate the evaluation criteria for 3D shape
reconstruction through behavioral studies, and use them to objectively and
systematically benchmark cutting-edge reconstruction algorithms on Pix3D.
Third, we design a novel model that simultaneously performs 3D reconstruction
and pose estimation; our multi-task learning approach achieves state-of-the-art
performance on both tasks.Comment: CVPR 2018. The first two authors contributed equally to this work.
Project page: http://pix3d.csail.mit.ed
- …