A generic test for the similarity of spatial data

Two spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research proposes a generic spatial similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. A simulation study is done in order to test the method for each spatial data set. After the simulation study, it was concluded that the proposed spatial similarity test is not sensitive to the user-defined resolution of the pixel image representation. From the simulation study, the proposed spatial similarity test performs well on lattice data, some of the unmarked point patterns and the marked point patterns with discrete marks. We illustrate this test on property prices in the City of Cape Town and the City of Johannesburg, South Africa

Subjective image quality assessment with boosted triplet comparisons.

In subjective full-reference image quality assessment, a reference image is distorted at increasing distortion levels. The differences between perceptual image qualities of the reference image and its distorted versions are evaluated, often using degradation category ratings (DCR). However, the DCR has been criticized since differences between rating categories on this ordinal scale might not be perceptually equidistant, and observers may have different understandings of the categories. Pair comparisons (PC) of distorted images, followed by Thurstonian reconstruction of scale values, overcomes these problems. In addition, PC is more sensitive than DCR, and it can provide scale values in fractional, just noticeable difference (JND) units that express a precise perceptional interpretation. Still, the comparison of images of nearly the same quality can be difficult. We introduce boosting techniques embedded in more general triplet comparisons (TC) that increase the sensitivity even more. Boosting amplifies the artefacts of distorted images, enlarges their visual representation by zooming, increases the visibility of the distortions by a flickering effect, or combines some of the above. Experimental results show the effectiveness of boosted TC for seven types of distortion (color diffusion, jitter, high sharpen, JPEG 2000 compression, lens blur, motion blur, multiplicative noise). For our study, we crowdsourced over 1.7 million responses to triplet questions. We give a detailed analysis of the data in terms of scale reconstructions, accuracy, detection rates, and sensitivity gain. Generally, boosting increases the discriminatory power and allows to reduce the number of subjective ratings without sacrificing the accuracy of the resulting relative image quality values. Our technique paves the way to fine-grained image quality datasets, allowing for more distortion levels, yet with high-quality subjective annotations. We also provide the details for Thurstonian scale reconstruction from TC and our annotated dataset, KonFiG-IQA , containing 10 source images, processed using 7 distortion types at 12 or even 30 levels, uniformly spaced over a span of 3 JND units

Optimizing Multiscale SSIM for Compression via MLDS

International audienceA crucial step in the assessment of an image compression method is the evaluation of the perceived quality of the compressed images. Typically, researchers ask observers to rate perceived image quality directly and use these rating measures, averaged across observers and images, to assess how image quality degrades with increasing compression. These ratings in turn are used to calibrate and compare image quality assessment algorithms intended to predict human perception of image degradation. There are several drawbacks to using such omnibus measures. First, the interpretation of the rating scale is subjective and may differ from one observer to the next. Second, it is easy to overlook compression artifacts that are only present in particular kinds of images. In this paper, we use a recently developed method for assessing perceived image quality, maximum likelihood difference scaling (MLDS), and use it to assess the performance of a widely-used image quality assessment algorithm, multiscale structural similarity (MS-SSIM). MLDS allows us to quantify supra-threshold perceptual differences between pairs of images and to examine how perceived image quality, estimated through MLDS, changes as the compression rate is increased. We apply the method to a wide range of images and also analyze results for specific images. This approach circumvents the limitations inherent in the use of rating methods, and allows us also to evaluate MS-SSIM for different classes of visual image. We show how the data collected by MLDS allow us to recalibrate MS-SSIM to improve its performance

Quantification of Order in Point Patterns

Pattern attributes are important in many disciplines, e.g. developmental biology, but there are few objective measures of them. Here we concentrate on the attribute of order in point patterns and its objective measurement. We examine perception of order and develop analysis algorithms that quantify the attribute in accordance with perception of it. Based on pairwise ranking of point patterns by degree of order, we show that judgements are highly consistent across individuals and that the perceptual dimension has an interval scale structure, spanning roughly 10 just-noticeable differences (jnds) between disorder and order. We designed a geometric algorithm that estimates order to an accuracy of half a jnd by quantifying the variability of the spaces between points. By anchoring the output of the algorithm so that Poisson point processes score on average 0, and perfect lattices score 10, we constructed an absolute interval scale of order. We demonstrated its utility in biology by quantifying the order of the Drosophila dorsal thorax epithelium during development. The psychophysical scaling method used relies on the comparison of stimuli with similar levels of order yielding a discrimination-based scale. As with other perceptual dimensions, an interesting question is whether supra-threshold perceptual differences are consistent with this scale. To test that we collected discrimination data, and data based on comparison of perceptual differences. Although the judgements of perceptual differences were found to be consistent with an interval scale, like the discrimination judgements, no common interval scale that could predict both sets of data was possible. Point patterns are commonly displayed as arrangements of dots. To examine how presentation parameters (dot size, dot numbers, and pattern area) affect discrimination, we collected discrimination data for ten presentation conditions. We found that discrimination performance depends on the ratio ‘dot diameter / average dot spacing’